Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey

Chengcheng Sun; Cheng Zhai; Jian Zhang; Jiayun Tian; Philip S. Yu; Xiaobin Rui; Yajie Song; Zhixiao Wang

REVIEW 2 major objections 7 minor 300 references

Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey

T0 review · 2 major / 7 minor · reviewed 2026-07-14 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A two-level taxonomy maps graph neural networks onto every stage of the knowledge-graph pipeline.

desk verdict Solid pipeline-wide GNN–KG survey with a usable two-level taxonomy; organizational value is real, novelty is incremental, and it is ready for referees as a reference piece. read the letter →

arxiv 2607.09666 v1 pith:W3TRW4HX submitted 2026-05-12 cs.LG cs.AIcs.SI

Chengcheng Sun , Jiayun Tian , Cheng Zhai , Zhixiao Wang , Yajie Song , Xiaobin Rui , Jian Zhang , Philip S. Yu This is my paper · ORCID

classification cs.LGcs.AIcs.SI

keywords KnowledgeGraphsGraphNeuralNetworksConstructionEmbeddingReasoningGCNGATHGNN

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that Graph Neural Networks have become central to knowledge graphs, yet no prior survey systematically covers GNN methods across the full knowledge-graph technology pipeline. It introduces a two-level taxonomy that crosses four lifecycle stages—construction, embedding, reasoning, and applications—with GNN model families such as GCN, GAT, and hypergraph networks. Under that grid it reviews how each architecture is adapted to tasks like entity extraction, alignment, static and temporal embedding, completion, forecasting, question answering, recommendation, and drug–drug interaction. A reader who works with knowledge graphs cares because the field has been fragmented; a shared map makes strengths, limits, and open gaps easier to compare and act on.

What carries the argument

The two-level taxonomy framework (knowledge-graph technologies pipeline × GNN-based perspective). It places every reviewed model into construction, embedding, reasoning, or applications and into families such as GCN, GAT, and HGNN so methods can be compared by task fit and architectural bias.

What would settle it

A large body of high-impact GNN–knowledge-graph work that cannot be assigned to construction, embedding, reasoning, or applications without force, or that depends on architectures outside the survey’s GCN/GAT/HGNN-style families and is essential to the pipeline, would show the taxonomy is incomplete.

Watch

Extended reading notes

Core claim

Prior reviews treated either knowledge graphs or GNNs in isolation, or only single tasks. This survey establishes that a two-level taxonomy—pipeline stage by GNN architecture—can organize the literature end-to-end, show where message-passing and attention help each stage, and surface unresolved challenges in multimodal, dynamic, low-resource, scalable, and industrial settings.

Load-bearing premise

That the four pipeline stages plus the chosen GNN families form a complete, non-overlapping map so every important paper can be cleanly placed.

Editorial extensions

If this is right

New GNN–knowledge-graph papers can be located on a shared grid instead of ad-hoc categories.
Task designers can match inductive biases (attention, hyperedges, temporal dynamics) to pipeline stages with clearer guidance.
Open challenges—multimodal fusion, real-time dynamic updates, few-shot learning, scalability, and industrial deployment—become shared targets.
Later surveys and benchmarks can reuse the same axes to check coverage.
Strengths and limits of representative models become easier to compare stage by stage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same grid can later absorb LLM–knowledge-graph hybrids as a cross-cutting layer once retrieval-augmented and graph-prompt methods stabilize.
If adopted, industrial platforms may catalog GNN modules by pipeline stage, lowering integration cost.
Coverage claims will be stress-tested by continuous-time or foundation-model graph methods that resist clean placement in the four stages or three GNN families.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a large, carefully organized survey of GNN methods across the full knowledge-graph pipeline. The one thing worth knowing is that its contribution is organizational: a two-level map (construction / embedding / reasoning / applications × GCN / GAT / HGNN and close variants) plus consistent tables that place dozens of models by graph-construction choice and architecture. It does not ship a new algorithm, theorem, or experiment.

What it does well is coverage and structure. Sections 4–7 walk through extraction, fusion, static/dynamic/complex embedding, completion vs. prediction reasoning, and applications (KGQA, recsys, NLP, DDI, KG-augmented LLMs). Tables 1–5 are usable; strengths/limitations are stated; open issues (multimodal, dynamic updates, low-resource, scale, industrial deployment, Graph Foundation Models) are current and clearly listed. Prior KG surveys, GNN surveys, and task-specific GNN–KG reviews are cited and distinguished, so the “first systematic pipeline-wide” claim is better read as “joint two-level organization across the whole lifecycle.” That is a legitimate, if modest, contribution.

Soft spots are the usual survey ones and stay proportional. Inclusion criteria are soft; the four-stage × GCN/GAT/HGNN partition is a useful map, not a complete non-overlapping partition of the field—some papers sit awkwardly or outside the bins. Occasional over-claim of uniqueness relative to earlier reviews is present but not load-bearing. No code, living bibliography, or quantitative meta-analysis. Citation pattern looks normal; minor self-citation of the authors’ earlier GNN work is not circular. Math is definitional (standard GCN/GAT updates), not novel, and is fine for the purpose.

Who it is for: anyone who needs a single entry point into GNN methods for KG construction through applications, or a student/postdoc mapping the subfield. It will not change practice outside that community, but it will reduce redundant literature trawling inside it.

I would send it to peer review. A serious editor should treat it as a reference survey that needs the novelty claim tightened and the taxonomy framed as one useful map among possible maps, not as a desk-reject. Engage with it as a working bibliography and organizing frame; cite it when you need the pipeline-wide view.

Referee Report

2 major / 7 minor

Summary. This survey argues that prior reviews have not systematically covered GNN methods across the full knowledge-graph technology pipeline, and proposes a two-level taxonomy (KG pipeline stages × GNN architectures / graph-construction styles) to fill that gap. The pipeline is partitioned into construction (extraction, fusion), embedding (static, dynamic, complex), reasoning (completion, prediction), and applications (KGQA, recommendation, NLP, DDI, KG-augmented LLMs). For each stage the authors review representative GNN models, emphasize graph-construction choices and architectures (GCN, GAT, HGNN and variants), summarize strengths and limitations, and close with open issues (multimodal fusion, dynamic updates, low-resource settings, scalability, industrial deployment) and future directions. Tables 1–5 map dozens of models to construction strategy and GNN type; Sections 2–3 fix notation and the taxonomy; Sections 4–7 deliver the stage-wise reviews.

Significance. If the organizational claim holds, the paper supplies a usable map of a large and fragmented literature that practitioners and newcomers can navigate by pipeline stage and GNN family. The consistent dual lens (graph construction + architecture) in Tables 1–5, the explicit strengths/limitations discussion, and the inclusion of recent KG–LLM work are concrete contributions. The work does not claim new algorithms or empirical results; its value is taxonomic and bibliographic. That is appropriate for a survey and is useful provided the coverage and selection criteria are transparent.

major comments (2)

Section 1 and the abstract assert a lack of systematic reviews of GNN methods across the entire KG pipeline and present the two-level taxonomy as filling that gap. Related surveys [35–41] are cited but only briefly distinguished. A short, explicit comparison table (scope, pipeline stages covered, GNN families, year) is needed so that the novelty claim is checkable rather than asserted. Without it, the central organizational claim remains under-supported.
Section 3 and Figure 4 introduce the four-stage pipeline and the GCN/GAT/HGNN families as the organizing frame, yet the manuscript never states inclusion/exclusion criteria, search strategy, or time window for the models in Tables 1–5. Soft or unstated selection criteria make it hard to assess completeness and bias (e.g., under-representation of spectral methods, Graph Transformers, or non-message-passing GNNs). A brief methods paragraph on how papers were chosen would make the taxonomy’s coverage claim falsifiable and strengthen the survey.

minor comments (7)

Figure 1 (timeline of KG review development) is rendered with heavily garbled text in the manuscript PDF; labels are unreadable. Redraw or re-export so the timeline is legible.
Figure 4 (taxonomy) and several other figures (e.g., Fig. 2 pipeline, Fig. 5 extraction diagrams) show similar encoding/OCR artifacts. Ensure all figures are clean vector or high-resolution exports.
Abstract and §1: “detailed review” / “we detailed review” — fix grammar (“we provide a detailed review”).
§2.1: triple notation G=(E1,R,E2) is nonstandard relative to the usual G=(E,R,T) or (h,r,t); a one-sentence alignment with conventional notation would help readers.
Tables 1–5 are dense and useful; adding a short caption note on how “GNN” and “Graph Construction” columns were assigned (author-reported vs. reclassified) would improve reproducibility of the taxonomy.
§8 open issues correctly flag multimodal fusion, dynamic KGs, low-resource settings, scalability, and industrial deployment; a sentence linking each open issue back to specific gaps in Tables 1–5 would tighten the connection between the review and the future-work agenda.
Occasional self-citations of the authors’ prior GNN work appear in the preliminaries; they are not load-bearing but should be balanced with standard GCN/GAT references already present.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: organizational survey taxonomy does not reduce any claim to its inputs by construction or self-citation chain.

full rationale

This is a literature survey whose central claim is organizational (a two-level taxonomy of KG pipeline stages × GNN families, plus a claim that prior reviews lack full-pipeline GNN coverage). There are no fitted parameters, no quantitative predictions, no uniqueness theorems, and no equations that equal their inputs by construction. The gap claim is supported by external surveys (e.g., [38–41] and earlier KG reviews), not by the authors’ own prior results. Occasional self-citations (e.g., the authors’ LGAT / heterogeneous-GNN papers) appear only as surveyed model instances inside Tables 1–5 and are not load-bearing for the taxonomy or the gap claim. Soft inclusion criteria and the completeness of the four-stage partition are ordinary survey limitations, not circular reductions. The derivation chain is therefore self-contained against external literature; score 0 with empty steps is the correct outcome.

Assumptions & free parameters 0 free parameters · 3 assumptions · 1 invented entities

As a survey the paper rests on standard definitions of knowledge graphs and GNNs, on a conventional four-stage KG pipeline, and on the authors’ choice of which GNN families and graph-construction styles to treat as primary. No numerical free parameters are fitted. The main invented construct is the two-level taxonomy itself.

assumptions (3)

domain assumption A knowledge graph is adequately formalized as a set of triples (or hyper-relational / multimodal extensions) whose nodes and edges can be processed by message-passing GNNs.
Stated in Section 2.1 and used throughout as the object of study.
domain assumption The KG technology lifecycle is exhaustively partitioned into construction, embedding, reasoning, and applications.
Introduced in Section 3 / Figure 4; every subsequent section is organized under this partition.
ad hoc to paper GCN, GAT, and HGNN (plus close variants) are the primary architectural families needed to categorize GNN–KG work.
Chosen as the second level of the taxonomy; other GNN styles are treated as secondary or folded into these bins.

invented entities (1)

Two-level taxonomy (KG pipeline × GNN architecture / graph-construction style)
purpose: To claim systematic coverage of GNN methods across the full knowledge-graph lifecycle and to structure the literature review.
Presented as the paper’s main organizational contribution (Abstract, Section 3, Figure 4). It is a classification scheme, not an independently measurable physical or computational object.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/W3TRW4HX

@misc{pith2026260709666,
  author       = {Pith},
  title        = {Pith review of: Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W3TRW4HX}},
  note         = {Machine review of arXiv:2607.09666}
}

read the original abstract

Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the entire knowledge graph technologies pipeline. To address this gap, we first propose a novel two-level taxonomy framework for GNN-based knowledge graph technologies: the KG technologies pipeline and GNN-based perspective. Specifically, the knowledge graph technologies pipeline covers knowledge graph construction, knowledge graph embedding, knowledge reasoning and knowledge graph applications. Meanwhile, the GNN-based perspective provides a new categorization of knowledge graph technologies with GNN models, such as GCN, GAT, and HGNN. Then, we analyze the advantages of GNN technology based on the characteristics of different tasks in the knowledge graph lifecycle. Furthermore, we detailed review various GNN-based models for knowledge graph following the proposed taxonomy, and summarize strengths and limitations. Finally, we discuss unresolved challenges and outline promising directions for future research.

Figures

Figures reproduced from arXiv: 2607.09666 by the authors.

**Figure 2.** Knowledge graph technologies pipeline. At present, a large number of knowledge graphs have emerged, of which the representative ones are OpenKN [44], CN-Dbpedia [45], DBpedia [46], Freebase [47], NELL [48], YAGO [49] and so on. In addition to the widespread development of open-domain knowledge graphs, many domain-specific knowledge graphs [50–54] have also been proposed and gradually constructed in recent years [PI… view at source ↗

**Figure 3.** GCN and GAT. Unlike the GCN uniform weighting operation, GAT [59] captures local information in the graph structure by introducing a self-attention mechanism in Figure 3b. This allows GAT to assign different weights to the connections between a node and its neighbors based on the relationships and feature similarities between the nodes [60]. The core idea of GAT is to compute attention coefficients for each neighbor… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Taxonomy of GNN-based knowledge graph techniques. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Different knowledge extraction tasks: Named Entity Recognition, Relation Extraction, and Joint Extraction. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Entity alignment and linking: (a) Entity alignment, identifying and matching equivalent entities in two knowledge graphs. (b) [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

**Figure 7.** Figure 7: A list of shapes of KGs (including Static KG, temporal KG, hyper-relation KG, and multimodal KG). [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

**Figure 8.** Figure 8: Inference for completion and prediction: (a) Example of knowledge graph completion. The dashed arrows are the relationships [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

**Figure 9.** Figure 9: Applications of knowledge graphs in AI systems [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

300 extracted references · 10 linked inside Pith

[1]

Knowledge graphs: Opportunities and challenges.Artificial Intelligence Review, 56(11):13071–13102, 2023

Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne. Knowledge graphs: Opportunities and challenges.Artificial Intelligence Review, 56(11):13071–13102, 2023

2023
[2]

Bolt defect classification algorithm based on knowledge graph and feature fusion.Energy Reports, 8:856–863, 2022

Yinghui Kong, Xu Liu, Zhenbing Zhao, Dongxia Zhang, and Jikun Duan. Bolt defect classification algorithm based on knowledge graph and feature fusion.Energy Reports, 8:856–863, 2022

2022
[3]

A survey of techniques for constructing chinese knowledge graphs and their applications

Tianxing Wu, Guilin Qi, Cheng Li, and Meng Wang. A survey of techniques for constructing chinese knowledge graphs and their applications. Sustainability, 10(9):3245, 2018

2018
[4]

A survey on cybersecurity knowledge graph construction.Computers & Security, 136:103524, 2024

Xiaojuan Zhao, Rong Jiang, Yue Han, Aiping Li, and Zhichao Peng. A survey on cybersecurity knowledge graph construction.Computers & Security, 136:103524, 2024

2024
[5]

Domain-specific knowledge graphs: A survey.Journal of Network and Computer Applications, 185:103076, 2021

Bilal Abu-Salih. Domain-specific knowledge graphs: A survey.Journal of Network and Computer Applications, 185:103076, 2021

2021
[6]

A decade of scholarly research on open knowledge graphs

Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, and Mohamed Ben Aouicha. A decade of scholarly research on open knowledge graphs. InLREC/COLING, pages 438–448. ELRA and ICCL, 2024

2024
[7]

Generative knowledge graph construction: A review

Hongbin Ye, Ningyu Zhang, Hui Chen, and Huajun Chen. Generative knowledge graph construction: A review. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors,Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1–17, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics

2022
[8]

Multi-modal knowledge graph construction and application: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(2):715–735, 2022

Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, and Nicholas Jing Yuan. Multi-modal knowledge graph construction and application: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(2):715–735, 2022

2022

Show all 300 references

[9]

Knowledge graphs.ACM Computing Surveys (Csur), 54(4):1–37, 2021

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, et al. Knowledge graphs.ACM Computing Surveys (Csur), 54(4):1–37, 2021

2021
[10]

A survey on knowledge graphs: Representation, acquisition, and applications.IEEE transactions on neural networks and learning systems, 33(2):494–514, 2021

Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. A survey on knowledge graphs: Representation, acquisition, and applications.IEEE transactions on neural networks and learning systems, 33(2):494–514, 2021

2021
[11]

A comprehensive survey on automatic knowledge graph construction.ACM Computing Surveys, 56(4):1–62, 2023

Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, and Xindong Wu. A comprehensive survey on automatic knowledge graph construction.ACM Computing Surveys, 56(4):1–62, 2023

2023
[12]

A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal.IEEE Trans

Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal.IEEE Trans. Pattern Anal. Mach. Intell., 46(12):9456–9478, 2024

2024
[13]

A comprehensive overview of knowledge graph completion.Knowledge-Based Systems, 255:109597, 2022

Tong Shen, Fu Zhang, and Jingwei Cheng. A comprehensive overview of knowledge graph completion.Knowledge-Based Systems, 255:109597, 2022

2022
[14]

Knowledge graph embedding: A survey from the perspective of representation spaces.ACM Computing Surveys, 56(6):1–42, 2024

Jiahang Cao, Jinyuan Fang, Zaiqiao Meng, and Shangsong Liang. Knowledge graph embedding: A survey from the perspective of representation spaces.ACM Computing Surveys, 56(6):1–42, 2024

2024
[15]

Nasrullah Khan, Zongmin Ma, Aman Ullah, and Kemal Polat. Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey.Expert Systems with Applications, 206:117737, 2022

2022
[16]

Multilingual question answering systems for knowledge graphs - a survey

Aleksandr Perevalov, Andreas Both, and Axel-Cyrille Ngonga Ngomo. Multilingual question answering systems for knowledge graphs - a survey. Semantic Web, 15(5):2089–2124, 2024
[17]

A survey on knowledge graph-based recommender systems.IEEE Transactions on Knowledge and Data Engineering, 34(8):3549–3568, 2020

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems.IEEE Transactions on Knowledge and Data Engineering, 34(8):3549–3568, 2020

2020
[18]

A survey on knowledge graph-based recommender systems : Extended abstract

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems : Extended abstract. InICDE, pages 3803–3804. IEEE, 2023

2023
[19]

Fusing external knowledge resources for natural language understanding techniques: A survey.Information Fusion, 92:190–204, 2023

Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De, and Amir Hussain. Fusing external knowledge resources for natural language understanding techniques: A survey.Information Fusion, 92:190–204, 2023

2023
[20]

A survey of knowledge enhanced pre-trained language models.IEEE Trans

Linmei Hu, Zeyi Liu, Ziwang Zhao, Lei Hou, Liqiang Nie, and Juanzi Li. A survey of knowledge enhanced pre-trained language models.IEEE Trans. Knowl. Data Eng., 36(4):1413–1430, 2024

2024
[21]

A decade of knowledge graphs in natural language processing: A survey

Phillip Schneider, Tim Schopf, Juraj Vladika, Michael Galkin, Elena Simperl, and Florian Matthes. A decade of knowledge graphs in natural language processing: A survey. InAACL/IJCNLP (1), pages 601–614. Association for Computational Linguistics, 2022

2022
[22]

Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of Big Data, 10(1):81, 2023

Bilal Abu-Salih, Muhammad Al-Qurishi, Mohammed Alweshah, Mohammad Al-Smadi, Reem Alfayez, and Heba Saadeh. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of Big Data, 10(1):81, 2023

2023
[23]

Knowledge-augmented graph machine learning for drug discovery: From precision to interpretability

Zhiqiang Zhong and Davide Mottin. Knowledge-augmented graph machine learning for drug discovery: From precision to interpretability. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5841–5842, 2023

2023
[24]

Knowledge graphs in education and employability: A survey on applications and techniques.IEEE Access, 10:80174–80183, 2022

Yousra Fettach, Mounir Ghogho, and Boualem Benatallah. Knowledge graphs in education and employability: A survey on applications and techniques.IEEE Access, 10:80174–80183, 2022

2022
[25]

Semantic knowledge graphs for the news: A review.ACM Computing Surveys, 55(7):1–38, 2022

Andreas L Opdahl, Tareq Al-Moslmi, Duc-Tien Dang-Nguyen, Marc Gallofré Ocaña, Bjørnar Tessem, and Csaba Veres. Semantic knowledge graphs for the news: A review.ACM Computing Surveys, 55(7):1–38, 2022

2022
[26]

News graph: An enhanced knowledge graph for news recommendation

Danyang Liu, Ting Bai, Jianxun Lian, Xin Zhao, Guangzhong Sun, Ji-Rong Wen, and Xing Xie. News graph: An enhanced knowledge graph for news recommendation. InKaRS@CIKM, volume 2601 ofCEUR Workshop Proceedings, pages 1–7. CEUR-WS.org, 2019

2019
[27]

Temporal knowledge graph completion: A survey

Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. Temporal knowledge graph completion: A survey. InIJCAI, pages 6545–6553. ijcai.org, 2023

2023
[28]

Knowledge graph quality management: A comprehensive survey.IEEE Trans

Bingcong Xue and Lei Zou. Knowledge graph quality management: A comprehensive survey.IEEE Trans. Knowl. Data Eng., 35(5):4969–4988, 2023. Manuscript submitted to ACM Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey 25

2023
[29]

Financial time series forecasting with multi-modality graph neural network.Pattern Recognition, 121:108218, 2022

Dawei Cheng, Fangzhou Yang, Sheng Xiang, and Jin Liu. Financial time series forecasting with multi-modality graph neural network.Pattern Recognition, 121:108218, 2022

2022
[30]

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18, 2024

Bharti Khemani, Shruti Patil, Ketan Kotecha, and Sudeep Tanwar. A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18, 2024

2024
[31]

A comprehensive survey on knowledge graph entity alignment via representation learning.arXiv preprint arXiv:2103.15059, pages 1–27, 2021

Rui Zhang, Bayu Distiawan Trisedy, Miao Li, Yong Jiang, and Jianzhong Qi. A comprehensive survey on knowledge graph entity alignment via representation learning.arXiv preprint arXiv:2103.15059, pages 1–27, 2021

2021 arXiv
[32]

A survey of graph neural network methods for relation extraction

Di Liu, Yungui Zhang, and Zhuoqing Li. A survey of graph neural network methods for relation extraction. In2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), volume 10, pages 2209–2223. IEEE, 2022

2022
[33]

A review: Knowledge reasoning over knowledge graph.Expert systems with applications, 141:112948, 2020

Xiaojun Chen, Shengbin Jia, and Yang Xiang. A review: Knowledge reasoning over knowledge graph.Expert systems with applications, 141:112948, 2020

2020
[34]

Deep learning on knowledge graph for recommender system: A survey.arXiv preprint arXiv:2004.00387, pages 1–21, 2020

Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, and Latifur Khan. Deep learning on knowledge graph for recommender system: A survey.arXiv preprint arXiv:2004.00387, pages 1–21, 2020

2004 arXiv
[35]

A survey on graph neural networks for knowledge graph completion.arXiv preprint arXiv:2007.12374, pages 1–10, 2020

Siddhant Arora. A survey on graph neural networks for knowledge graph completion.arXiv preprint arXiv:2007.12374, pages 1–10, 2020

2007 arXiv
[36]

Graph neural networks to improve knowledge graph embedding: A survey

Ismail Chetoui, Essaid El Bachari, Mohammed El Adnani, and Abdelwahed El Hassan. Graph neural networks to improve knowledge graph embedding: A survey. InInternational Conference on Advanced Intelligent Systems for Sustainable Development, pages 15–25. Springer, 2022

2022
[37]

A review of graph neural networks and pretrained language models for knowledge graph reasoning.Neurocomputing, page 128490, 2024

Jiangtao Ma, Bo Liu, Kunlin Li, Chenliang Li, Fan Zhang, Xiangyang Luo, and Yaqiong Qiao. A review of graph neural networks and pretrained language models for knowledge graph reasoning.Neurocomputing, page 128490, 2024

2024
[38]

Entity alignment via graph neural networks: a component-level study

Yanfeng Shu, Ji Zhang, Guangyan Huang, Chi-Hung Chi, and Jing He. Entity alignment via graph neural networks: a component-level study. World Wide Web, 26(6):4069–4092, 2023

2023
[39]

Graph intelligence with large language models and prompt learning

Jia Li, Xiangguo Sun, Yuhan Li, Zhixun Li, Hong Cheng, and Jeffrey Xu Yu. Graph intelligence with large language models and prompt learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 6545–6554, 2024

2024
[40]

A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9456–9478, 2024

Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12...

2024
[41]

What is event knowledge graph: A survey.IEEE Transactions on Knowledge and Data Engineering, 35(7):7569–7589, 2022

Saiping Guan, Xueqi Cheng, Long Bai, Fujun Zhang, Zixuan Li, Yutao Zeng, Xiaolong Jin, and Jiafeng Guo. What is event knowledge graph: A survey.IEEE Transactions on Knowledge and Data Engineering, 35(7):7569–7589, 2022

2022
[42]

Introducing the knowledge graph: things, not strings.Official google blog, 5(16):3, 2012

Amit Singhal et al. Introducing the knowledge graph: things, not strings.Official google blog, 5(16):3, 2012

2012
[43]

Learning structured embeddings of knowledge bases

Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. Learning structured embeddings of knowledge bases. InProceedings of the AAAI conference on artificial intelligence, number 1 in 25, pages 301–306, 2011

2011
[44]

Openkn: An open knowledge computational engine for network big data

Yantao Jia, Yuanzhuo Wang, Xueqi Cheng, Xiaolong Jin, and Jiafeng Guo. Openkn: An open knowledge computational engine for network big data. In2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pages 657–664. IEEE, 2014

2014
[45]

Cn-dbpedia: A never-ending chinese knowledge extraction system

Bo Xu, Yong Xu, Jiaqing Liang, Chenhao Xie, Bin Liang, Wanyun Cui, and Yanghua Xiao. Cn-dbpedia: A never-ending chinese knowledge extraction system. InInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 428–438. Spri...

2017
[46]

Dbpedia: A nucleus for a web of open data

Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. Dbpedia: A nucleus for a web of open data. In international semantic web conference, pages 722–735. Springer, 2007

2007
[47]

Freebase: a collaboratively created graph database for structuring human knowledge

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. InProceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1247–1250, 2008

2008
[48]

Toward an architecture for never-ending language learning

Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam Hruschka, and Tom Mitchell. Toward an architecture for never-ending language learning. InProceedings of the AAAI conference on artificial intelligence, number 1 in 24, pages 1306–1313, 2010

2010
[49]

Yago3: A knowledge base from multilingual wikipedias

Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek. Yago3: A knowledge base from multilingual wikipedias. InCIDR, pages 1–12, 2013

2013
[50]

The harmonic memory: a knowledge graph of harmonic patterns as a trustworthy framework for computational creativity

Jacopo de Berardinis, Albert Meroño-Peñuela, Andrea Poltronieri, and Valentina Presutti. The harmonic memory: a knowledge graph of harmonic patterns as a trustworthy framework for computational creativity. InProceedings of the ACM Web Conference 2023, pages 3873–3882, 2023

2023
[51]

The world literature knowledge graph

Marco Antonio Stranisci, Eleonora Bernasconi, Viviana Patti, Stefano Ferilli, Miguel Ceriani, and Rossana Damiano. The world literature knowledge graph. InInternational Semantic Web Conference, pages 435–452. Springer, 2023

2023
[52]

The holocaust archival material knowledge graph

Herminio García-González and Mike Bryant. The holocaust archival material knowledge graph. InInternational Semantic Web Conference, pages 362–379. Springer, 2023

2023
[53]

Kg-treat: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs

Ruoqi Liu, Lingfei Wu, and Ping Zhang. Kg-treat: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs. InProceedings of the AAAI Conference on Artificial Intelligence, number 8 in 38, pages 8805–8814, 2024

2024
[54]

Seqcare: Sequential training with external medical knowledge graph for diagnosis prediction in healthcare data

Yongxin Xu, Xu Chu, Kai Yang, Zhiyuan Wang, Peinie Zou, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. Seqcare: Sequential training with external medical knowledge graph for diagnosis prediction in healthcare data. InProceedings of the ACM Web Conference 2023, pages 281...

2023
[55]

A new model for learning in graph domains

Marco Gori, Gabriele Monfardini, and Franco Scarselli. A new model for learning in graph domains. InProceedings. 2005 IEEE international joint conference on neural networks, 2005., volume 2, pages 729–734. IEEE, 2005

2005
[56]

Deep learning on graphs: A survey.IEEE Transactions on Knowledge and Data Engineering, 34(1):249–270, 2020

Ziwei Zhang, Peng Cui, and Wenwu Zhu. Deep learning on graphs: A survey.IEEE Transactions on Knowledge and Data Engineering, 34(1):249–270, 2020. Manuscript submitted to ACM 26 C. Sun et al

2020
[57]

A comprehensive survey on graph neural networks

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020

2020
[58]

Uzair Aslam Bhatti, Hao Tang, Guilu Wu, Shah Marjan, and Aamir Hussain. Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence.International Journal of Intelligent Systems, 2023(1):8342104, 2023

2023
[59]

Graph attention networks.stat, 1050(20):10–48550, 2017

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks.stat, 1050(20):10–48550, 2017

2017
[60]

Attention-based graph neural networks: a survey.Artif

Chengcheng Sun, Chenhao Li, Xiang Lin, Tianji Zheng, Fanrong Meng, Xiaobin Rui, and Zhixiao Wang. Attention-based graph neural networks: a survey.Artif. Intell. Rev., 56(Suppl 2):2263–2310, August 2023

2023
[61]

Lgat: a light graph attention network focusing on message passing for semi-supervised node classification.Computing, 106(6):1729–1747, 2024

Chengcheng Sun, Fanrong Meng, Chenhao Li, Xiaobin Rui, and Zhixiao Wang. Lgat: a light graph attention network focusing on message passing for semi-supervised node classification.Computing, 106(6):1729–1747, 2024

2024
[62]

A supervised multi-head self-attention network for nested named entity recognition

Yongxiu Xu, Heyan Huang, Chong Feng, and Yue Hu. A supervised multi-head self-attention network for nested named entity recognition. In Proceedings of the AAAI conference on artificial intelligence, number 16 in 35, pages 14185–14193, 2021

2021
[63]

Mapre: An effective semantic mapping approach for low-resource relation extraction

Manqing Dong, Chunguang Pan, and Zhipeng Luo. Mapre: An effective semantic mapping approach for low-resource relation extraction. In EMNLP (1), pages 2694–2704. Association for Computational Linguistics, 2021

2021
[64]

Joint extraction of entities and relations based on a novel decomposition strategy

Bowen Yu, Zhenyu Zhang, Xiaobo Shu, Tingwen Liu, Yubin Wang, Bin Wang, and Sujian Li. Joint extraction of entities and relations based on a novel decomposition strategy. InECAI 2020, pages 2282–2289. IOS Press, 2020

2020
[65]

Toward practical entity alignment method design: Insights from new highly heterogeneous knowledge graph datasets

Xuhui Jiang, Chengjin Xu, Yinghan Shen, Yuanzhuo Wang, Fenglong Su, Zhichao Shi, Fei Sun, Zixuan Li, Jian Guo, and Huawei Shen. Toward practical entity alignment method design: Insights from new highly heterogeneous knowledge graph datasets. InProceedings of the ACM on Web Con...

2024
[66]

Tempel: Linking dynamically evolving and newly emerging entities.Advances in Neural Information Processing Systems, 35:1850–1866, 2022

Klim Zaporojets, Lucie-Aimée Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, and Isabelle Augenstein. Tempel: Linking dynamically evolving and newly emerging entities.Advances in Neural Information Processing Systems, 35:1850–1866, 2022

2022
[67]

Multiview feature augmented neural network for knowledge graph embedding.Knowledge- Based Systems, 255:109721, 2022

Dan Jiang, Ronggui Wang, Lixia Xue, and Juan Yang. Multiview feature augmented neural network for knowledge graph embedding.Knowledge- Based Systems, 255:109721, 2022

2022
[68]

Neural latent space model for dynamic networks and temporal knowledge graphs

Tony Gracious, Shubham Gupta, Arun Kanthali, Rui M Castro, and Ambedkar Dukkipati. Neural latent space model for dynamic networks and temporal knowledge graphs. InProceedings of the AAAI Conference on Artificial Intelligence, number 5 in 35, pages 4054–4062, 2021

2021
[69]

Hyperbolic hypergraph neural networks for multi-relational knowledge hypergraph representation.CoRR, abs/2412.12158:1–13, 2024

Mengfan Li, Xuanhua Shi, Chenqi Qiao, Teng Zhang, and Hai Jin. Hyperbolic hypergraph neural networks for multi-relational knowledge hypergraph representation.CoRR, abs/2412.12158:1–13, 2024

2024 arXiv
[70]

Message passing for hyper-relational knowledge graphs

Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann. Message passing for hyper-relational knowledge graphs. InEMNLP (1), pages 7346–7359. Association for Computational Linguistics, 2020

2020
[71]

Aspectmmkg: A multi-modal knowledge graph with aspect-aware entities

Jingdan Zhang, Jiaan Wang, Xiaodan Wang, Zhixu Li, and Yanghua Xiao. Aspectmmkg: A multi-modal knowledge graph with aspect-aware entities. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 3361–3370, 2023

2023
[72]

Renn: A rule embedding enhanced neural network framework for temporal knowledge graph completion

Linlin Zong, Zhenrong Xie, Chi Ma, Xinyue Liu, Xianchao Zhang, and Bo Xu. Renn: A rule embedding enhanced neural network framework for temporal knowledge graph completion. InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources...

2024
[73]

Confidence is not timeless: Modeling temporal validity for rule-based temporal knowledge graph forecasting

Rikui Huang, Wei Wei, Xiaoye Qu, Shengzhe Zhang, Dangyang Chen, and Yu Cheng. Confidence is not timeless: Modeling temporal validity for rule-based temporal knowledge graph forecasting. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (...

2024
[74]

Multi-granularity temporal question answering over knowledge graphs

Ziyang Chen, Jinzhi Liao, and Xiang Zhao. Multi-granularity temporal question answering over knowledge graphs. InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11378–11392, 2023

2023
[75]

Unleashing the power of knowledge graph for recommendation via invariant learning

Shuyao Wang, Yongduo Sui, Chao Wang, and Hui Xiong. Unleashing the power of knowledge graph for recommendation via invariant learning. In Proceedings of the ACM on Web Conference 2024, pages 3745–3755, 2024

2024
[76]

Hallucination mitigation in natural language generation from large-scale open-domain knowledge graphs

Xiao Shi, Zhengyuan Zhu, Zeyu Zhang, and Chengkai Li. Hallucination mitigation in natural language generation from large-scale open-domain knowledge graphs. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12506–12521, 2023

2023
[77]

Kgnn: Knowledge graph neural network for drug-drug interaction prediction

Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. Kgnn: Knowledge graph neural network for drug-drug interaction prediction. InIJCAI, volume 380, pages 2739–2745, 2020

2020
[78]

Nested named entity recognition with partially-observed treecrfs

Yao Fu, Chuanqi Tan, Mosha Chen, Songfang Huang, and Fei Huang. Nested named entity recognition with partially-observed treecrfs. InAAAI, pages 12839–12847. AAAI Press, 2021

2021
[79]

Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, and Philip S. Yu. Gradient imitation reinforcement learning for low resource relation extraction. InEMNLP (1), pages 2737–2746. Association for Computational Linguistics, 2021

2021
[80]

A partition filter network for joint entity and relation extraction

Zhiheng Yan, Chong Zhang, Jinlan Fu, Qi Zhang, and Zhongyu Wei. A partition filter network for joint entity and relation extraction. InEMNLP (1), pages 185–197. Association for Computational Linguistics, 2021

2021
[81]

Leverage lexical knowledge for chinese named entity recognition via collaborative graph network

Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. Leverage lexical knowledge for chinese named entity recognition via collaborative graph network. InProceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joi...

2019
[82]

A neural multi-digraph model for chinese ner with gazetteers

Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, and Luo Si. A neural multi-digraph model for chinese ner with gazetteers. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 1462–1467, 2019. Manuscript submitted to ACM ...

2019
[83]

Polymorphic graph attention network for chinese ner.Expert Systems with Applications, 203:117467, 2022

Yuke Wang, Ling Lu, Yang Wu, and Yinong Chen. Polymorphic graph attention network for chinese ner.Expert Systems with Applications, 203:117467, 2022

2022
[84]

Enhanced named entity recognition based on multi-feature fusion using dual graph neural networks

HZ Gu, JL Ma, YR Zhao, and A Khadka. Enhanced named entity recognition based on multi-feature fusion using dual graph neural networks. Acadlore Trans. AI Mach. Learn., 3(2):84–93, 2024

2024
[85]

Learning common dependency structure for unsupervised cross-domain ner

Luchen Liu, Xixun Lin, Peng Zhang, Lei Zhang, and Bin Wang. Learning common dependency structure for unsupervised cross-domain ner. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8347–8351. IEEE, 2022

2022
[86]

Gnner: Reducing overlapping in span-based ner using graph neural networks

Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. Gnner: Reducing overlapping in span-based ner using graph neural networks. InProceedings of the 60th annual meeting of the Association for Computational Linguistics: student research workshop, pages 97–103, 2022

2022
[87]

Randomly wired graph neural network for chinese ner.Expert Systems with Applications, 227:120245, 2023

Jie Chen, Xuefeng Xi, Victor S Sheng, and Zhiming Cui. Randomly wired graph neural network for chinese ner.Expert Systems with Applications, 227:120245, 2023

2023
[88]

A lexicon-based graph neural network for chinese ner

Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei, and Xuan-Jing Huang. A lexicon-based graph neural network for chinese ner. InProceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference ...

2019
[89]

A multi-channel graph attention network for chinese ner

Yichun Zhao, Kui Meng, and Gongshen Liu. A multi-channel graph attention network for chinese ner. InInternational Conference on Neural Information Processing, pages 203–214. Springer, 2021

2021
[90]

Knowledge-aware named entity recognition with alleviating heterogeneity

Binling Nie, Ruixue Ding, Pengjun Xie, Fei Huang, Chen Qian, and Luo Si. Knowledge-aware named entity recognition with alleviating heterogeneity. InProceedings of the AAAI Conference on Artificial Intelligence, number 15 in 35, pages 13595–13603, 2021

2021
[91]

Semantic-enhanced graph neural network for named entity recognition in ancient chinese books.Scientific Reports, 14(1):17488, 2024

Yongrui Xu, Caixia Mao, Zhiyong Wang, Guonian Jin, liangji Zhong, and Tao Qian. Semantic-enhanced graph neural network for named entity recognition in ancient chinese books.Scientific Reports, 14(1):17488, 2024

2024
[92]

Heterogeneous graph neural network with relation-aware label propagation for unbalanced node classification.Physica A: Statistical Mechanics and its Applications, 660:130369, 2025

Chengcheng Sun, Cheng Zhai, Qihan Feng, Xiaobin Rui, and Zhixiao Wang. Heterogeneous graph neural network with relation-aware label propagation for unbalanced node classification.Physica A: Statistical Mechanics and its Applications, 660:130369, 2025

2025
[93]

Trigger-gnn: a trigger-based graph neural network for nested named entity recognition

Yuan Sui, Fanyang Bu, Yingting Hu, Liang Zhang, and Wei Yan. Trigger-gnn: a trigger-based graph neural network for nested named entity recognition. In2022 International Joint Conference on Neural Networks (IJCNN), pages 01–08. IEEE, 2022

2022
[94]

Chinese nested entity recognition method for the finance domain based on heterogeneous graph network.Information Processing & Management, 61(5):103812, 2024

Han Zhang, Yiping Dang, Yazhou Zhang, Siyuan Liang, Junxiu Liu, and Lixia Ji. Chinese nested entity recognition method for the finance domain based on heterogeneous graph network.Information Processing & Management, 61(5):103812, 2024

2024
[95]

Learning from different text-image pairs: a relation-enhanced graph convolutional network for multimodal ner

Fei Zhao, Chunhui Li, Zhen Wu, Shangyu Xing, and Xinyu Dai. Learning from different text-image pairs: a relation-enhanced graph convolutional network for multimodal ner. InProceedings of the 30th ACM international conference on multimedia, pages 3983–3992, 2022

2022
[96]

Few-shot multimodal named entity recognition based on mutlimodal causal intervention graph

Feihong Lu, Xiaocui Yang, Qian Li, Qingyun Sun, Ke Jiang, Cheng Ji, and Jianxin Li. Few-shot multimodal named entity recognition based on mutlimodal causal intervention graph. InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resou...

2024
[97]

Knowing false negatives: An adversarial training method for distantly supervised relation extraction

Kailong Hao, Botao Yu, and Wei Hu. Knowing false negatives: An adversarial training method for distantly supervised relation extraction. In EMNLP (1), pages 9661–9672. Association for Computational Linguistics, 2021

2021
[98]

Yuhao Zhang, Peng Qi, and Christopher D. Manning. Graph convolution over pruned dependency trees improves relation extraction. InEMNLP, pages 2205–2215. Association for Computational Linguistics, 2018

2018
[99]

Relation extraction with convolutional network over learnable syntax- transport graph

Kai Sun, Richong Zhang, Yongyi Mao, Samuel Mensah, and Xudong Liu. Relation extraction with convolutional network over learnable syntax- transport graph. InProceedings of the AAAI conference on artificial intelligence, number 05 in 34, pages 8928–8935, 2020

2020
[100]

Attention guided graph convolutional networks for relation extraction

Zhijiang Guo, Yan Zhang, and Wei Lu. Attention guided graph convolutional networks for relation extraction. InACL (1), pages 241–251. Association for Computational Linguistics, 2019

2019
[101]

Improve relation extraction with dual attention-guided graph convolutional networks.Neural Computing and Applications, 33:1773–1784, 2021

Zhixin Li, Yaru Sun, Jianwei Zhu, Suqin Tang, Canlong Zhang, and Huifang Ma. Improve relation extraction with dual attention-guided graph convolutional networks.Neural Computing and Applications, 33:1773–1784, 2021

2021
[102]

Dependency-driven relation extraction with attentive graph convolutional networks

Yuanhe Tian, Guimin Chen, Yan Song, and Xiang Wan. Dependency-driven relation extraction with attentive graph convolutional networks. In ACL/IJCNLP (1), pages 4458–4471. Association for Computational Linguistics, 2021

2021
[103]

Connecting the dots: Document-level neural relation extraction with edge-oriented graphs

Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. Connecting the dots: Document-level neural relation extraction with edge-oriented graphs. InEMNLP/IJCNLP (1), pages 4924–4935. Association for Computational Linguistics, 2019

2019
[104]

Document-level relation extraction with reconstruction

Wang Xu, Kehai Chen, and Tiejun Zhao. Document-level relation extraction with reconstruction. InProceedings of the AAAI Conference on Artificial Intelligence, number 16 in 35, pages 14167–14175, 2021

2021
[105]

Global-to-local neural networks for document-level relation extraction

Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun. Global-to-local neural networks for document-level relation extraction. InEMNLP (1), pages 3711–3721. Association for Computational Linguistics, 2020

2020
[106]

Double graph based reasoning for document-level relation extraction

Shuang Zeng, Runxin Xu, Baobao Chang, and Lei Li. Double graph based reasoning for document-level relation extraction. InEMNLP (1), pages 1630–1640. Association for Computational Linguistics, 2020

2020
[107]

Few-shot relation extraction with dual graph neural network interaction.IEEE Trans

Jing Li, Shanshan Feng, and Billy Chiu. Few-shot relation extraction with dual graph neural network interaction.IEEE Trans. Neural Networks Learn. Syst., 35(10):14396–14408, 2024

2024
[108]

RECON: relation extraction using knowledge graph context in a graph neural network

Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, and Manohar Kaul. RECON: relation extraction using knowledge graph context in a graph neural network. InWWW, pages 1673–1685. ACM / IW3C2, 2021

2021
[109]

Gdpnet: Refining latent multi-view graph for relation extraction

Fuzhao Xue, Aixin Sun, Hao Zhang, and Eng Siong Chng. Gdpnet: Refining latent multi-view graph for relation extraction. InProceedings of the AAAI Conference on Artificial Intelligence, number 16 in 35, pages 14194–14202, 2021. Manuscript submitted to ACM 28 C. Sun et al

2021
[110]

Graph neural networks with generated parameters for relation extraction

Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. Graph neural networks with generated parameters for relation extraction. InACL (1), pages 1331–1339. Association for Computational Linguistics, 2019

2019
[111]

Reasoning with latent structure refinement for document-level relation extraction

Guoshun Nan, Zhijiang Guo, Ivan Sekulic, and Wei Lu. Reasoning with latent structure refinement for document-level relation extraction. InACL, pages 1546–1557. Association for Computational Linguistics, 2020

2020
[112]

A unified multi-task learning framework for joint extraction of entities and relations

Tianyang Zhao, Zhao Yan, Yunbo Cao, and Zhoujun Li. A unified multi-task learning framework for joint extraction of entities and relations. In Proceedings of the AAAI Conference on Artificial Intelligence, number 16 in 35, pages 14524–14531, 2021

2021
[113]

Joint type inference on entities and relations via graph convolutional networks

Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, and Nan Duan. Joint type inference on entities and relations via graph convolutional networks. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages ...

2019
[114]

Named entity recognition and relation extraction with graph neural networks in semi structured documents

Manuel Carbonell, Pau Riba, Mauricio Villegas, Alicia Fornés, and Josep Lladós. Named entity recognition and relation extraction with graph neural networks in semi structured documents. In2020 25th International Conference on Pattern Recognition (ICPR), pages 9622–9627. IEEE, 2021

2021
[115]

Graphrel: Modeling text as relational graphs for joint entity and relation extraction

Tsu-Jui Fu, Peng-Hsuan Li, and Wei-Yun Ma. Graphrel: Modeling text as relational graphs for joint entity and relation extraction. InProceedings of the 57th annual meeting of the association for computational linguistics, pages 1409–1418, 2019

2019
[116]

Conditional probability joint extraction of nested biomedical events: design of a unified extraction framework based on neural networks.JMIR Medical Informatics, 10(6):e37804, 2022

Yan Wang, Jian Wang, Huiyi Lu, Bing Xu, Yijia Zhang, Santosh Kumar Banbhrani, Hongfei Lin, et al. Conditional probability joint extraction of nested biomedical events: design of a unified extraction framework based on neural networks.JMIR Medical Informatics, 10(6):e37804, 2022

2022
[117]

A novel table-to-graph generation approach for document-level joint entity and relation extraction

Ruoyu Zhang, Yanzeng Li, and Lei Zou. A novel table-to-graph generation approach for document-level joint entity and relation extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10853–10865, 2023

2023
[118]

Joint entity and relation extraction model based on directed-relation gat oriented to chinese patent texts.Soft Computing, pages 1–18, 2024

Yushan Zhao, Kuan-Ching Li, Tengke Wang, and Shunxiang Zhang. Joint entity and relation extraction model based on directed-relation gat oriented to chinese patent texts.Soft Computing, pages 1–18, 2024

2024
[119]

A joint entity and relation extraction model based on efficient sampling and explicit interaction.ACM Transactions On Intelligent Systems And Technology, 14(5):1–18, 2023

Qibin Li, Nianmin Yao, Nai Zhou, Jian Zhao, and Yanan Zhang. A joint entity and relation extraction model based on efficient sampling and explicit interaction.ACM Transactions On Intelligent Systems And Technology, 14(5):1–18, 2023

2023
[120]

Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction.Knowledge-Based Systems, 219:106888, 2021

Kang Zhao, Hua Xu, Yue Cheng, Xiaoteng Li, and Kai Gao. Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction.Knowledge-Based Systems, 219:106888, 2021

2021
[121]

Chemical-gene relation extraction with graph neural networks and bert encoder

Mina Esmail Zadeh Nojoo Kambar, Armin Esmaeilzadeh, and Kazem Taghva. Chemical-gene relation extraction with graph neural networks and bert encoder. InThe International Conference on Innovations in Computing Research, pages 166–179. Springer, 2022

2022
[122]

Joint entity and relation extraction with span pruning and hypergraph neural networks

Zhaohui Yan, Songlin Yang, Wei Liu, and Kewei Tu. Joint entity and relation extraction with span pruning and hypergraph neural networks. In EMNLP, pages 7512–7526. Association for Computational Linguistics, 2023

2023
[123]

Multi-view graph neural architecture search for biomedical entity and relation extraction.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2):1221–1233, 2022

Raeed Al-Sabri, Jianliang Gao, Jiamin Chen, Babatounde Moctard Oloulade, and Tengfei Lyu. Multi-view graph neural architecture search for biomedical entity and relation extraction.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2):1221–1233, 2022

2022
[124]

Banglaautokg: Automatic bangla knowledge graph construction with semantic neural graph filtering

Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, and Dong-Kyu Chae. Banglaautokg: Automatic bangla knowledge graph construction with semantic neural graph filtering. InLREC/COLING, pages 2100–2106. ELRA and ICCL, 2024

2024
[125]

Bootstrapping entity alignment with knowledge graph embedding

Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. Bootstrapping entity alignment with knowledge graph embedding. InIJCAI, pages 4396–4402. ijcai.org, 2018

2018
[126]

Suchanek, Serge Abiteboul, and Pierre Senellart

Fabian M. Suchanek, Serge Abiteboul, and Pierre Senellart. PARIS: probabilistic alignment of relations, instances, and schema.Proc. VLDB Endow., 5(3):157–168, 2011

2011
[127]

Automatic interlinking of music datasets on the semantic web.LDOW, 369:1–8, 2008

Yves Raimond, Christopher Sutton, and Mark B Sandler. Automatic interlinking of music datasets on the semantic web.LDOW, 369:1–8, 2008

2008
[128]

Coordinated reasoning for cross-lingual knowledge graph alignment

Kun Xu, Linfeng Song, Yansong Feng, Yan Song, and Dong Yu. Coordinated reasoning for cross-lingual knowledge graph alignment. InProceedings of the AAAI conference on artificial intelligence, number 05 in 34, pages 9354–9361, 2020

2020
[129]

Relation-aware neighborhood matching model for entity alignment

Yao Zhu, Hongzhi Liu, Zhonghai Wu, and Yingpeng Du. Relation-aware neighborhood matching model for entity alignment. InProceedings of the AAAI Conference on Artificial Intelligence, number 5 in 35, pages 4749–4756, 2021

2021
[130]

Mraea: an efficient and robust entity alignment approach for cross-lingual knowledge graph

Xin Mao, Wenting Wang, Huimin Xu, Man Lan, and Yuanbin Wu. Mraea: an efficient and robust entity alignment approach for cross-lingual knowledge graph. InProceedings of the 13th International Conference on Web Search and Data Mining, pages 420–428, 2020

2020
[131]

Jointly learning entity and relation representations for entity alignment

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, and Dongyan Zhao. Jointly learning entity and relation representations for entity alignment. In EMNLP/IJCNLP (1), pages 240–249. Association for Computational Linguistics, 2019

2019
[132]

A vectorized relational graph convolutional network for multi-relational network alignment

Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, and Mingzhong Wang. A vectorized relational graph convolutional network for multi-relational network alignment. InIJCAI, volume 2019, pages 4135–4141, 2019

2019
[133]

Relation-aware entity alignment for heterogeneous knowledge graphs

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, and Dongyan Zhao. Relation-aware entity alignment for heterogeneous knowledge graphs. InIJCAI, pages 5278–5284. ijcai.org, 2019

2019
[134]

Knowledge graph alignment with entity-pair embedding

Zhichun Wang, Jinjian Yang, and Xiaoju Ye. Knowledge graph alignment with entity-pair embedding. InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1672–1680, 2020

2020
[135]

Knowledge graph alignment network with gated multi-hop neighborhood aggregation

Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, and Yuzhong Qu. Knowledge graph alignment network with gated multi-hop neighborhood aggregation. InProceedings of the AAAI conference on artificial intelligence, number 01 in 34, pages 222–229, 2020

2020
[136]

Neighborhood matching network for entity alignment

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, and Dongyan Zhao. Neighborhood matching network for entity alignment. InACL, pages 6477–6487. Association for Computational Linguistics, 2020

2020
[137]

Multi-channel graph neural network for entity alignment

Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, and Tat-Seng Chua. Multi-channel graph neural network for entity alignment. In ACL (1), pages 1452–1461. Association for Computational Linguistics, 2019. Manuscript submitted to ACM Knowledge Graphs Meet Graph Neur...

2019
[138]

Global structure and local semantics-preserved embeddings for entity alignment

Chi Man Wong, Qiang Chen, Suhui Wu, and Wei Zhang. Global structure and local semantics-preserved embeddings for entity alignment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, volume 20, pages 3658–3664, 2020

2020
[139]

Exploring and evaluating attributes, values, and structures for entity alignment

Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, and Tat-Seng Chua. Exploring and evaluating attributes, values, and structures for entity alignment. InEMNLP (1), pages 6355–6364. Association for Computational Linguistics, 2020

2020
[140]

Attribute-consistent knowledge graph representation learning for multi-modal entity alignment

Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, and Jianxin Li. Attribute-consistent knowledge graph representation learning for multi-modal entity alignment. InProceedings of the ACM Web Conference 2023, pages 2499–2508, 2023

2023
[141]

Visual pivoting for (unsupervised) entity alignment

Fangyu Liu, Muhao Chen, Dan Roth, and Nigel Collier. Visual pivoting for (unsupervised) entity alignment. InProceedings of the AAAI conference on artificial intelligence, number 5 in 35, pages 4257–4266, 2021

2021
[142]

Aligning cross-lingual entities with multi-aspect information

Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, and Xu Sun. Aligning cross-lingual entities with multi-aspect information. In EMNLP/IJCNLP (1), pages 4430–4440. Association for Computational Linguistics, 2019

2019
[143]

Tea: time-aware entity alignment in knowledge graphs

Yu Liu, Wen Hua, Kexuan Xin, Saeid Hosseini, and Xiaofang Zhou. Tea: time-aware entity alignment in knowledge graphs. InProceedings of the ACM Web Conference 2023, pages 2591–2599, 2023

2023
[144]

A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs

Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, and Man Lan. A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs. InCOLING, pages 2075–2086. International Committee on Computational Linguistics, 2022
[145]

Time-aware graph neural networks for entity alignment between temporal knowledge graphs

Chengjin Xu, Fenglong Su, and Jens Lehmann. Time-aware graph neural networks for entity alignment between temporal knowledge graphs. CoRR, abs/2203.02150:1–12, 2022

2022 arXiv
[146]

Unsupervised entity alignment for temporal knowledge graphs

Xiaoze Liu, Junyang Wu, Tianyi Li, Lu Chen, and Yunjun Gao. Unsupervised entity alignment for temporal knowledge graphs. InProceedings of the ACM Web Conference 2023, pages 2528–2538, 2023

2023
[147]

Dynamic knowledge graph alignment

Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, and Hanghang Tong. Dynamic knowledge graph alignment. InProceedings of the AAAI conference on artificial intelligence, number 5 in 35, pages 4564–4572, 2021

2021
[148]

Large-scale entity alignment via knowledge graph merging, partitioning and embedding

Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Jianfeng Qu, and Xiaofang Zhou. Large-scale entity alignment via knowledge graph merging, partitioning and embedding. InProceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 2240–2249, 2022

2022
[149]

Activeea: Active learning for neural entity alignment

Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, and Genghong Zhao. Activeea: Active learning for neural entity alignment. InEMNLP (1), pages 3364–3374. Association for Computational Linguistics, 2021

2021
[150]

Lambda: Learning matchable prior for entity alignment with unlabeled dangling cases

Hang Yin, Liyao Xiang, Dong Ding, Yuheng He, Yihan Wu, Pengzhi Chu, Xinbing Wang, and Chenghu Zhou. Lambda: Learning matchable prior for entity alignment with unlabeled dangling cases. InThe Thirty-eighth Annual Conference on Neural Information Processing Systems, pages 1–32, 2024

2024
[151]

Graph neural networks for entity matching

Evgeny Krivosheev, Katsiaryna Mirylenka, Mattia Atzeni, and Paolo Scotton. Graph neural networks for entity matching. InIEEE Big Data, pages 6212–6214. IEEE, 2023

2023
[152]

Self-supervised enhancement for named entity disambiguation via multimodal graph convolution.IEEE Trans

Pengfei Zhou, Kaining Ying, Zhenhua Wang, Dongyan Guo, and Cong Bai. Self-supervised enhancement for named entity disambiguation via multimodal graph convolution.IEEE Trans. Neural Networks Learn. Syst., 35(1):231–245, 2024

2024
[153]

Graph neural entity disambiguation.Knowl

Linmei Hu, Jiayu Ding, Chuan Shi, Chao Shao, and Shaohua Li. Graph neural entity disambiguation.Knowl. Based Syst., 195:105620, 2020

2020
[154]

Dynamic graph convolutional networks for entity linking

Junshuang Wu, Richong Zhang, Yongyi Mao, Hongyu Guo, Masoumeh Soflaei, and Jinpeng Huai. Dynamic graph convolutional networks for entity linking. InWWW, pages 1149–1159. ACM / IW3C2, 2020

2020
[155]

High quality candidate generation and sequential graph attention network for entity linking

Zheng Fang, Yanan Cao, Ren Li, Zhenyu Zhang, Yanbing Liu, and Shi Wang. High quality candidate generation and sequential graph attention network for entity linking. InWWW, pages 640–650. ACM / IW3C2, 2020

2020
[156]

Cogcn: Combining co-attention with graph convolutional network for entity linking with knowledge graphs.Expert Syst

Ningning Jia, Xiang Cheng, Sen Su, and Liyuan Ding. Cogcn: Combining co-attention with graph convolutional network for entity linking with knowledge graphs.Expert Syst. J. Knowl. Eng., 38(1):1–13, 2021

2021
[157]

Collective entity linking with joint subgraphs

Kedong Wang, Yu Xia, and Fang Kong. Collective entity linking with joint subgraphs. InNLPCC (2), volume 14303 ofLecture Notes in Computer Science, pages 173–184. Springer, 2023

2023
[158]

Integrating manifold knowledge for global entity linking with heterogeneous graphs

Zhibin Chen, Yuting Wu, Yansong Feng, and Dongyan Zhao. Integrating manifold knowledge for global entity linking with heterogeneous graphs. Data Intell., 4(1):20–40, 2022

2022
[159]

Two-stage text-to-bimql semantic parsing for building information model extraction using graph neural networks.Automation in Construction, 152:104902, 2023

Mengtian Yin, Llewellyn Tang, Chris Webster, Jinyang Li, Haotian Li, Zhuoquan Wu, and Reynold CK Cheng. Two-stage text-to-bimql semantic parsing for building information model extraction using graph neural networks.Automation in Construction, 152:104902, 2023

2023
[160]

Medical entity disambiguation using graph neural networks

Alina Vretinaris, Chuan Lei, Vasilis Efthymiou, Xiao Qin, and Fatma Özcan. Medical entity disambiguation using graph neural networks. In SIGMOD Conference, pages 2310–2318. ACM, 2021

2021
[161]

Medical entity disambiguation with medical mention relation and fine-grained entity knowledge

Wenpeng Lu, Guobiao Zhang, Xueping Peng, Hongjiao Guan, and Shoujin Wang. Medical entity disambiguation with medical mention relation and fine-grained entity knowledge. InLREC/COLING, pages 11148–11158. ELRA and ICCL, 2024

2024
[162]

Translating embeddings for modeling multi- relational data

Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi- relational data. InNIPS, pages 2787–2795, 2013

2013
[163]

Knowledge graph embedding by translating on hyperplanes

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. InAAAI, pages 1112–1119. AAAI Press, 2014

2014
[164]

Learning entity and relation embeddings for knowledge graph completion

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. In AAAI, pages 2181–2187. AAAI Press, 2015

2015
[165]

Embedding entities and relations for learning and inference in knowledge bases

Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. InICLR (Poster), pages 1–12, 2015. Manuscript submitted to ACM 30 C. Sun et al

2015
[166]

Complex embeddings for simple link prediction

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In ICML, volume 48 ofJMLR Workshop and Conference Proceedings, pages 2071–2080. JMLR.org, 2016
[167]

Modeling relational data with graph convolutional networks

Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. InThe semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, proceedings ...

2018
[168]

Robust embedding with multi-level structures for link prediction

Zihan Wang, Zhaochun Ren, Chunyu He, Peng Zhang, and Yue Hu. Robust embedding with multi-level structures for link prediction. InIJCAI, pages 5240–5246, 2019

2019
[169]

Message function search for knowledge graph embedding

Shimin Di and Lei Chen. Message function search for knowledge graph embedding. InProceedings of the ACM Web Conference 2023, pages 2633–2644, 2023

2023
[170]

How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view

Ren Li, Yanan Cao, Qiannan Zhu, Guanqun Bi, Fang Fang, Yi Liu, and Qian Li. How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view. InProceedings of the AAAI conference on artificial intelligence, number 5 in 36, pages 5781–5791, 2022

2022
[171]

Entity-agnostic representation learning for parameter-efficient knowledge graph embedding

Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z Pan, and Huajun Chen. Entity-agnostic representation learning for parameter-efficient knowledge graph embedding. InProceedings of the AAAI Conference on Artificial Intelligence, number 4 in 37, pages 4182–4190, 2023

2023
[172]

Distributed representations of entities in open-world knowledge graphs.Knowledge-Based Systems, 290:111582, 2024

Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpu Bo, Yin Fang, Xiaoze Liu, Huajun Chen, and Wen Zhang. Distributed representations of entities in open-world knowledge graphs.Knowledge-Based Systems, 290:111582, 2024

2024
[173]

Heterogeneous graph neural network with hypernetworks for knowledge graph embedding

Xiyang Liu, Tong Zhu, Huobin Tan, and Richong Zhang. Heterogeneous graph neural network with hypernetworks for knowledge graph embedding. InInternational Semantic Web Conference, pages 284–302. Springer, 2022

2022
[174]

Inductive relation prediction by subgraph reasoning

Komal Teru, Etienne Denis, and Will Hamilton. Inductive relation prediction by subgraph reasoning. InInternational Conference on Machine Learning, pages 9448–9457. PMLR, 2020

2020
[175]

Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs

Xiaohan Xu, Peng Zhang, Yongquan He, Chengpeng Chao, and Chaoyang Yan. Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs. InIJCAI, pages 2341–2347. ijcai.org, 2022

2022
[176]

Communicative message passing for inductive relation reasoning

Sijie Mai, Shuangjia Zheng, Yuedong Yang, and Haifeng Hu. Communicative message passing for inductive relation reasoning. InAAAI, pages 4294–4302. AAAI Press, 2021

2021
[177]

Ingram: Inductive knowledge graph embedding via relation graphs

Jaejun Lee, Chanyoung Chung, and Joyce Jiyoung Whang. Ingram: Inductive knowledge graph embedding via relation graphs. InICML, volume 202 ofProceedings of Machine Learning Research, pages 18796–18809. PMLR, 2023

2023
[178]

Disconnected emerging knowledge graph oriented inductive link prediction

Yufeng Zhang, Weiqing Wang, Hongzhi Yin, Pengpeng Zhao, Wei Chen, and Lei Zhao. Disconnected emerging knowledge graph oriented inductive link prediction. InICDE, pages 381–393. IEEE, 2023

2023
[179]

Temporal knowledge graph reasoning based on evolutional representation learning

Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. Temporal knowledge graph reasoning based on evolutional representation learning. InSIGIR, pages 408–417. ACM, 2021

2021
[180]

GTRL: an entity group-aware temporal knowledge graph representation learning method.IEEE Trans

Xing Tang and Ling Chen. GTRL: an entity group-aware temporal knowledge graph representation learning method.IEEE Trans. Knowl. Data Eng., 36(9):4707–4721, 2024

2024
[181]

Hamilton

Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. Temp: Temporal message passing for temporal knowledge graph completion. InEMNLP (1), pages 5730–5746. Association for Computational Linguistics, 2020

2020
[182]

DACHA: A dual graph convolution based temporal knowledge graph representation learning method using historical relation.ACM Trans

Ling Chen, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, and Yongjun Zhang. DACHA: A dual graph convolution based temporal knowledge graph representation learning method using historical relation.ACM Trans. Knowl. Discov. Data, 16(3):46:1–46:18, 2022

2022
[183]

Explainable subgraph reasoning for forecasting on temporal knowledge graphs

Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. InICLR, pages 1–24. OpenReview.net, 2021

2021
[184]

TARGAT: A time-aware relational graph attention model for temporal knowledge graph embedding.IEEE ACM Trans

Zhiwen Xie, Runjie Zhu, Jin Liu, Guangyou Zhou, and Jimmy Xiangji Huang. TARGAT: A time-aware relational graph attention model for temporal knowledge graph embedding.IEEE ACM Trans. Audio Speech Lang. Process., 31:2246–2258, 2023

2023
[185]

Local-global history-aware contrastive learning for temporal knowledge graph reasoning

Wei Chen, Huaiyu Wan, Yuting Wu, Shuyuan Zhao, Jiayaqi Cheng, Yuxin Li, and Youfang Lin. Local-global history-aware contrastive learning for temporal knowledge graph reasoning. InICDE, pages 733–746. IEEE, 2024

2024
[186]

On the representation and embedding of knowledge bases beyond binary relations

Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, and Richong Zhang. On the representation and embedding of knowledge bases beyond binary relations. InIJCAI, pages 1300–1307. IJCAI/AAAI Press, 2016

2016
[187]

Knowledge hypergraphs: Prediction beyond binary relations

Bahare Fatemi, Perouz Taslakian, David Vázquez, and David Poole. Knowledge hypergraphs: Prediction beyond binary relations. InIJCAI, pages 2191–2197. ijcai.org, 2020

2020
[188]

Neuinfer: Knowledge inference on n-ary facts

Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. Neuinfer: Knowledge inference on n-ary facts. InACL, pages 6141–6151. Association for Computational Linguistics, 2020

2020
[189]

Heterogeneous hypergraph variational autoencoder for link prediction.IEEE Trans

Haoyi Fan, Fengbin Zhang, Yuxuan Wei, Zuoyong Li, Changqing Zou, Yue Gao, and Qionghai Dai. Heterogeneous hypergraph variational autoencoder for link prediction.IEEE Trans. Pattern Anal. Mach. Intell., 44(8):4125–4138, 2022

2022
[190]

DHGE: dual-view hyper-relational knowledge graph embedding for link prediction and entity typing

Haoran Luo, Haihong E, Ling Tan, Gengxian Zhou, Tianyu Yao, and Kaiyang Wan. DHGE: dual-view hyper-relational knowledge graph embedding for link prediction and entity typing. InAAAI, pages 6467–6474. AAAI Press, 2023

2023
[191]

Learning representations for hyper-relational knowledge graphs

Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, and Hui Liu. Learning representations for hyper-relational knowledge graphs. InASONAM, pages 253–257. ACM, 2023

2023
[192]

Contrastive multi-modal knowledge graph representation learning.IEEE Trans

Quan Fang, Xiaowei Zhang, Jun Hu, Xian Wu, and Changsheng Xu. Contrastive multi-modal knowledge graph representation learning.IEEE Trans. Knowl. Data Eng., 35(9):8983–8996, 2023. Manuscript submitted to ACM Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey 31

2023
[193]

Hyper-node relational graph attention network for multi-modal knowledge graph completion.ACM Trans

Shuang Liang, Anjie Zhu, Jiasheng Zhang, and Jie Shao. Hyper-node relational graph attention network for multi-modal knowledge graph completion.ACM Trans. Multim. Comput. Commun. Appl., 19(2):62:1–62:21, 2023

2023
[194]

A comprehensive overview of knowledge graph completion.Knowl

Tong Shen, Fu Zhang, and Jingwei Cheng. A comprehensive overview of knowledge graph completion.Knowl. Based Syst., 255:109597, 2022

2022
[195]

Quaternion knowledge graph embeddings

Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu. Quaternion knowledge graph embeddings. InNeurIPS, pages 2731–2741, 2019

2019
[196]

Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution

Xinyuan Chen, Zhongmei Zhou, Meichun Gao, Daya Shi, and Mohd N Husen. Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution. InUncertainty in Artificial Intelligence, pages 336–345. PMLR, 2022

2022
[197]

Inductive knowledge graph completion with gnns and rules: An analysis

Akash Anil, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, and Steven Schockaert. Inductive knowledge graph completion with gnns and rules: An analysis. InLREC/COLING, pages 9036–9049. ELRA and ICCL, 2024

2024
[198]

Phung, and Dat Quoc Nguyen

Vinh Tong, Dai Quoc Nguyen, Dinh Q. Phung, and Dat Quoc Nguyen. Two-view graph neural networks for knowledge graph completion. In ESWC, volume 13870 ofLecture Notes in Computer Science, pages 262–278. Springer, 2023

2023
[199]

Phung, and Dat Quoc Nguyen

Dai Quoc Nguyen, Vinh Tong, Dinh Q. Phung, and Dat Quoc Nguyen. Node co-occurrence based graph neural networks for knowledge graph link prediction. InWSDM, pages 1589–1592. ACM, 2022

2022
[200]

Kostylev

Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, and Egor V. Kostylev. INDIGO: gnn-based inductive knowledge graph completion using pair-wise encoding. InNeurIPS, pages 2034–2045, 2021
[201]

Arnold, Bing Xiang, Philip S

Shen Wang, Xiaokai Wei, Cícero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang, Philip S. Yu, and Isabel F. Cruz. Mixed-curvature multi-relational graph neural network for knowledge graph completion. InWWW, pages 1761–1771. ACM / IW3C2, 2021

2021
[202]

Exploring & exploiting high-order graph structure for sparse knowledge graph completion.Frontiers Comput

Tao He, Ming Liu, Yixin Cao, Zekun Wang, Zihao Zheng, and Bing Qin. Exploring & exploiting high-order graph structure for sparse knowledge graph completion.Frontiers Comput. Sci., 19(2):192306, 2025

2025
[203]

Aggarwal, and Jiliang Tang

Harry Shomer, Yao Ma, Juanhui Li, Bo Wu, Charu C. Aggarwal, and Jiliang Tang. Distance-based propagation for efficient knowledge graph reasoning. InEMNLP, pages 14692–14707. Association for Computational Linguistics, 2023

2023
[204]

Relational graph neural network with hierarchical attention for knowledge graph completion

Zhao Zhang, Fuzhen Zhuang, Hengshu Zhu, Zhi-Ping Shi, Hui Xiong, and Qing He. Relational graph neural network with hierarchical attention for knowledge graph completion. InAAAI, pages 9612–9619. AAAI Press, 2020

2020
[205]

Enhancing heterogeneous knowledge graph completion with a novel gat-based approach.ACM Trans

Wanxu Wei, Yitong Song, and Bin Yao. Enhancing heterogeneous knowledge graph completion with a novel gat-based approach.ACM Trans. Knowl. Discov. Data, 18(4):104:1–104:20, 2024

2024
[206]

MRGAT: multi-relational graph attention network for knowledge graph completion.Neural Networks, 154:234–245, 2022

Guoquan Dai, Xizhao Wang, Xiaoying Zou, Chao Liu, and Si Cen. MRGAT: multi-relational graph attention network for knowledge graph completion.Neural Networks, 154:234–245, 2022

2022
[207]

Disenkgat: Knowledge graph embedding with disentangled graph attention network

Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, and Xiangnan He. Disenkgat: Knowledge graph embedding with disentangled graph attention network. InCIKM, pages 2140–2149. ACM, 2021

2021
[208]

Graph attention network with dynamic representation of relations for knowledge graph completion.Expert Syst

Xin Zhang, Chunxia Zhang, Jingtao Guo, Cheng Peng, Zhendong Niu, and Xindong Wu. Graph attention network with dynamic representation of relations for knowledge graph completion.Expert Syst. Appl., 219:119616, 2023

2023
[209]

KRACL: contrastive learning with graph context modeling for sparse knowledge graph completion

Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, and Minnan Luo. KRACL: contrastive learning with graph context modeling for sparse knowledge graph completion. InWWW, pages 2548–2559. ACM, 2023

2023
[210]

Decoupling mixture-of-graphs: Unseen relational learning for knowledge graph completion by fusing ontology and textual experts

Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Zhengtao Yu, and Jun Zhao. Decoupling mixture-of-graphs: Unseen relational learning for knowledge graph completion by fusing ontology and textual experts. InCOLING, pages 2237–2246. International Committee on Comput...

2022
[211]

Chengke Wu, Xiao Li, Rui Jiang, Yuanjun Guo, Jun Wang, and Zhile Yang. Graph-based deep learning model for knowledge base completion in constraint management of construction projects.Computer-Aided Civil and Infrastructure Engineering, 38(6):702–719, 2023

2023
[212]

Multilingual knowledge graph completion with language-sensitive multi-graph attention

Rongchuan Tang, Yang Zhao, Chengqing Zong, and Yu Zhou. Multilingual knowledge graph completion with language-sensitive multi-graph attention. InACL (1), pages 10508–10519. Association for Computational Linguistics, 2023

2023
[213]

Association rules enhanced knowledge graph attention network.Knowl

Zhenghao Zhang, Jianbin Huang, and Qinglin Tan. Association rules enhanced knowledge graph attention network.Knowl. Based Syst., 239:108038, 2022

2022
[214]

Knowledge graph completion with counterfactual augmentation

Heng Chang, Jie Cai, and Jia Li. Knowledge graph completion with counterfactual augmentation. InWWW, pages 2611–2620. ACM, 2023

2023
[215]

Learning from counterfactual links for link prediction

Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang. Learning from counterfactual links for link prediction. InICML, volume 162 of Proceedings of Machine Learning Research, pages 26911–26926. PMLR, 2022

2022
[216]

Recurrent event network: Autoregressive structure inference over temporal knowledge graphs

Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. arXiv preprint arXiv:1904.05530, pages 1–15, 2019

1904 arXiv
[217]

Temporal knowledge graph reasoning with dynamic hypergraph embedding

Xinyue Liu, Jianan Zhang, Chi Ma, Wenxin Liang, Bo Xu, and Linlin Zong. Temporal knowledge graph reasoning with dynamic hypergraph embedding. InLREC/COLING, pages 15742–15751. ELRA and ICCL, 2024

2024
[218]

Learning latent relations for temporal knowledge graph reasoning

Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, and Liang Wang. Learning latent relations for temporal knowledge graph reasoning. InACL (1), pages 12617–12631. Association for Computational Linguistics, 2023

2023
[219]

Learn from relational correlations and periodic events for temporal knowledge graph reasoning

Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. Learn from relational correlations and periodic events for temporal knowledge graph reasoning. InSIGIR, pages 1559–1568. ACM, 2023

2023
[220]

Evokg: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs

Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong. Evokg: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs. InWSDM, pages 794–803. ACM, 2022

2022
[221]

Intricate spatiotemporal dependency learning for temporal knowledge graph reasoning.ACM Trans

Xuefei Li, Huiwei Zhou, Weihong Yao, Wenchu Li, Baojie Liu, and Yingyu Lin. Intricate spatiotemporal dependency learning for temporal knowledge graph reasoning.ACM Trans. Knowl. Discov. Data, 18(6):136:1–136:19, 2024. Manuscript submitted to ACM 32 C. Sun et al

2024
[222]

Meta-learning based knowledge extrapolation for temporal knowledge graph

Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, and Yong Dou. Meta-learning based knowledge extrapolation for temporal knowledge graph. InWWW, pages 2433–2443. ACM, 2023

2023
[223]

Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning

Yujia Li, Shiliang Sun, and Jing Zhao. Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. InIJCAI, pages 2152–2158. ijcai.org, 2022

2022
[224]

Learning long- and short-term representations for temporal knowledge graph reasoning

Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, and Liang Wang. Learning long- and short-term representations for temporal knowledge graph reasoning. InWWW, pages 2412–2422. ACM, 2023

2023
[225]

Hismatch: Historical structure matching based temporal knowledge graph reasoning

Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, and Xueqi Cheng. Hismatch: Historical structure matching based temporal knowledge graph reasoning. InFindings of the Association for Computational Linguistics: EMNLP 202...

2022
[226]

Temporal inductive path neural network for temporal knowledge graph reasoning.Artif

Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, and Yuanchun Zhou. Temporal inductive path neural network for temporal knowledge graph reasoning.Artif. Intell., 329:104085, 2024

2024
[227]

Learning neural ordinary equations for forecasting future links on temporal knowledge graphs

Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. InEMNLP (1), pages 8352–8364. Association for Computational Linguistics, 2021

2021
[228]

Learning to walk across time for interpretable temporal knowledge graph completion

Jaehun Jung, Jinhong Jung, and U Kang. Learning to walk across time for interpretable temporal knowledge graph completion. InKDD, pages 786–795. ACM, 2021

2021
[229]

Tpmod: A tendency-guided prediction model for temporal knowledge graph completion.ACM Trans

Luyi Bai, Xiangnan Ma, Mingcheng Zhang, and Wenting Yu. Tpmod: A tendency-guided prediction model for temporal knowledge graph completion.ACM Trans. Knowl. Discov. Data, 15(3):41:1–41:17, 2021

2021
[230]

Da-net: Distributed attention network for temporal knowledge graph reasoning

Kangzheng Liu, Feng Zhao, Hongxu Chen, Yicong Li, Guandong Xu, and Hai Jin. Da-net: Distributed attention network for temporal knowledge graph reasoning. InCIKM, pages 1289–1298. ACM, 2022

2022
[231]

Taret: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion.Inf

Jiangtao Ma, Kunlin Li, Fan Zhang, Yanjun Wang, Xiangyang Luo, Chenliang Li, and Yaqiong Qiao. Taret: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion.Inf. Process. Manag., 61(6):103848, 2024

2024
[232]

Temporal knowledge graph representation learning with local and global evolutions

Jiasheng Zhang, Shuang Liang, Yongpan Sheng, and Jie Shao. Temporal knowledge graph representation learning with local and global evolutions. Knowl. Based Syst., 251:109234, 2022

2022
[233]

Hierarchical graph attention network for temporal knowledge graph reasoning.Neurocomputing, 550:126390, 2023

Pengpeng Shao, Jiayi He, Guanjun Li, Dawei Zhang, and Jianhua Tao. Hierarchical graph attention network for temporal knowledge graph reasoning.Neurocomputing, 550:126390, 2023

2023
[234]

Interpreting temporal knowledge graph reasoning (student abstract)

Bin Chen, Kai Yang, Wenxin Tai, Zhangtao Cheng, Leyuan Liu, Ting Zhong, and Fan Zhou. Interpreting temporal knowledge graph reasoning (student abstract). InAAAI, pages 23451–23453. AAAI Press, 2024

2024
[235]

Learning multi-graph structure for temporal knowledge graph reasoning.Expert Syst

Jinchuan Zhang, Bei Hui, Chong Mu, and Ling Tian. Learning multi-graph structure for temporal knowledge graph reasoning.Expert Syst. Appl., 255:124561, 2024

2024
[236]

Crnet: Modeling concurrent events over temporal knowledge graph

Shichao Wang, Xiangrui Cai, Ying Zhang, and Xiaojie Yuan. Crnet: Modeling concurrent events over temporal knowledge graph. InISWC, volume 13489 ofLecture Notes in Computer Science, pages 516–533. Springer, 2022

2022
[237]

Reasoninglm: Enabling structural subgraph reasoning in pre-trained language models for question answering over knowledge graph

Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Yaliang Li, and Ji-Rong Wen. Reasoninglm: Enabling structural subgraph reasoning in pre-trained language models for question answering over knowledge graph. InEMNLP, pages 3721–3735. Association for Computational Linguistics, 2023

2023
[238]

Benchmark and neural architecture for conversational entity retrieval from a knowledge graph

Mona Zamiri, Yao Qiang, Fedor Nikolaev, Dongxiao Zhu, and Alexander Kotov. Benchmark and neural architecture for conversational entity retrieval from a knowledge graph. InWWW, pages 1519–1528. ACM, 2024

2024
[239]

Hierarchy-aware multi-hop question answering over knowledge graphs

Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, and Zhimeng Jiang. Hierarchy-aware multi-hop question answering over knowledge graphs. InProceedings of the ACM web conference 2023, pages 2519–2527, 2023

2023
[240]

Jointlk: Joint reasoning with language models and knowledge graphs for commonsense question answering

Yueqing Sun, Qi Shi, Le Qi, and Yu Zhang. Jointlk: Joint reasoning with language models and knowledge graphs for commonsense question answering. InNAACL-HLT, pages 5049–5060. Association for Computational Linguistics, 2022

2022
[241]

Acenet: Attention guided commonsense reasoning on hybrid knowledge graph

Chuzhan Hao, Minghui Xie, and Peng Zhang. Acenet: Attention guided commonsense reasoning on hybrid knowledge graph. InEMNLP, pages 8461–8471. Association for Computational Linguistics, 2022

2022
[242]

Boyd-Graber

Chen Zhao, Chenyan Xiong, Xin Qian, and Jordan L. Boyd-Graber. Complex factoid question answering with a free-text knowledge graph. In WWW, pages 1205–1216. ACM / IW3C2, 2020

2020
[243]

QA-GNN: reasoning with language models and knowledge graphs for question answering

Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. QA-GNN: reasoning with language models and knowledge graphs for question answering. InNAACL-HLT, pages 535–546. Association for Computational Linguistics, 2021

2021
[244]

Subgraph retrieval enhanced model for multi-hop knowledge base question answering

Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, and Hong Chen. Subgraph retrieval enhanced model for multi-hop knowledge base question answering. InACL (1), pages 5773–5784. Association for Computational Linguistics, 2022

2022
[245]

Knowledge base question answering by case-based reasoning over subgraphs

Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, and Andrew McCallum. Knowledge base question answering by case-based reasoning over subgraphs. InICML, volume 162 ofProceedings of Machine Learning Research, pages 4777–4793....

2022
[246]

QAGCN: answering multi-relation questions via single-step implicit reasoning over knowledge graphs

Ruijie Wang, Luca Rossetto, Michael Cochez, and Abraham Bernstein. QAGCN: answering multi-relation questions via single-step implicit reasoning over knowledge graphs. InESWC (1), volume 14664 ofLecture Notes in Computer Science, pages 41–58. Springer, 2024

2024
[247]

Complex temporal question answering on knowledge graphs

Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. Complex temporal question answering on knowledge graphs. InProceedings of the 30th ACM international conference on information & knowledge management, pages 792–802, 2021

2021
[248]

Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Toward subgraph-guided knowledge graph question generation with graph neural networks.IEEE Trans. Neural Networks Learn. Syst., 35(9):12706–12717, 2024. Manuscript submitted to ACM Knowledge Graphs Meet Graph Neural Networks: A Compre...

2024
[249]

Continuous-time sequential recommendation with temporal graph collaborative transformer

Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S Yu. Continuous-time sequential recommendation with temporal graph collaborative transformer. InProceedings of the 30th ACM international conference on information & knowledge management, pages 433–442, 2021

2021
[250]

Ziwei Fan, Zhiwei Liu, Shen Wang, Lei Zheng, and Philip S. Yu. Modeling sequences as distributions with uncertainty for sequential recommendation. InCIKM, pages 3019–3023. ACM, 2021

2021
[251]

Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation

Liangwei Yang, Zhiwei Liu, Yingtong Dou, Jing Ma, and Philip S Yu. Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation. InProceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval, pages 2...

2021
[252]

Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. InAAAI, pages 4486–4493. AAAI Press, 2021

2021
[253]

Ye Liu, Yao Wan, Lifang He, Hao Peng, and Philip S. Yu. KG-BART: knowledge graph-augmented BART for generative commonsense reasoning. In AAAI, pages 6418–6425. AAAI Press, 2021

2021
[254]

Knowledge graph convolutional networks for recommender systems

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. Knowledge graph convolutional networks for recommender systems. InWWW, pages 3307–3313. ACM, 2019

2019
[255]

SSAR-GNN: self-supervised artist recommendation from spatio-temporal perspectives in art history with graph neural networks.Future Gener

Qinglin Zhang, Menghan Wang, Haiyan Wang, Xuan Rao, and Lisi Chen. SSAR-GNN: self-supervised artist recommendation from spatio-temporal perspectives in art history with graph neural networks.Future Gener. Comput. Syst., 144:230–241, 2023

2023
[256]

Fi-gnn: Modeling feature interactions via graph neural networks for CTR prediction

Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. Fi-gnn: Modeling feature interactions via graph neural networks for CTR prediction. InCIKM, pages 539–548. ACM, 2019

2019
[257]

Knowledge-aware coupled graph neural network for social recommendation

Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, and Yanfang Ye. Knowledge-aware coupled graph neural network for social recommendation. InAAAI, pages 4115–4122. AAAI Press, 2021

2021
[258]

Ziwei Fan, Zhiwei Liu, Shelby Heinecke, Jianguo Zhang, Huan Wang, Caiming Xiong, and Philip S. Yu. Zero-shot item-based recommendation via multi-task product knowledge graph pre-training. InCIKM, pages 483–493. ACM, 2023

2023
[259]

ATBRG: adaptive target-behavior relational graph network for effective recommendation

Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, and Wenwu Ou. ATBRG: adaptive target-behavior relational graph network for effective recommendation. InSIGIR, pages 2231–2240. ACM, 2020

2020
[260]

Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, and Philip S. Yu. Dskreg: Differentiable sampling on knowledge graph for recommendation with relational GNN. InCIKM, pages 3513–3517. ACM, 2021

2021
[261]

Graphsw: a training protocol based on stage-wise training for gnn-based recommender model.CoRR, abs/1908.05611:1–6, 2019

Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, and Shao-Yu Chu. Graphsw: a training protocol based on stage-wise training for gnn-based recommender model.CoRR, abs/1908.05611:1–6, 2019

1908 arXiv
[262]

SARC: split-and-recombine networks for knowledge-based recommendation

Weifeng Zhang, Yi Cao, and Congfu Xu. SARC: split-and-recombine networks for knowledge-based recommendation. InICTAI, pages 652–659. IEEE, 2019

2019
[263]

KLGCN: knowledge graph-aware light graph convolutional network for recommender systems.Expert Syst

Fei Wang, Yansheng Li, Yongjun Zhang, and Dong Wei. KLGCN: knowledge graph-aware light graph convolutional network for recommender systems.Expert Syst. Appl., 195:116513, 2022

2022
[264]

KGAT: knowledge graph attention network for recommendation

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. KGAT: knowledge graph attention network for recommendation. InKDD, pages 950–958. ACM, 2019

2019
[265]

Contextualized graph attention network for recommendation with item knowledge graph.IEEE Trans

Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, and Juyong Zhang. Contextualized graph attention network for recommendation with item knowledge graph.IEEE Trans. Knowl. Data Eng., 35(1):181–195, 2023

2023
[266]

Hagerec: Hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation.Knowl

Zuoxi Yang and Shoubin Dong. Hagerec: Hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation.Knowl. Based Syst., 204:106194, 2020

2020
[267]

Learning intents behind interactions with knowledge graph for recommendation

Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. Learning intents behind interactions with knowledge graph for recommendation. InWWW, pages 878–887. ACM / IW3C2, 2021

2021
[268]

Disentangled interest importance aware knowledge graph neural network for fund recommendation

Ke Tu, Wei Qu, Zhengwei Wu, Zhiqiang Zhang, Zhongyi Liu, Yiming Zhao, Le Wu, Jun Zhou, and Guannan Zhang. Disentangled interest importance aware knowledge graph neural network for fund recommendation. InCIKM, pages 2482–2491. ACM, 2023

2023
[269]

Knowledge-aware graph neural networks with label smoothness regularization for recommender systems

Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. InKDD, pages 968–977. ACM, 2019

2019
[270]

Self-supervised dynamic hypergraph recommendation based on hyper-relational knowledge graph

Yi Liu, Hongrui Xuan, Bohan Li, Meng Wang, Tong Chen, and Hongzhi Yin. Self-supervised dynamic hypergraph recommendation based on hyper-relational knowledge graph. InCIKM, pages 1617–1626. ACM, 2023

2023
[271]

Vijaikumar, Deepesh V

M. Vijaikumar, Deepesh V. Hada, and Shirish K. Shevade. Hypertenet: Hypergraph and transformer-based neural network for personalized list continuation. InICDM, pages 1210–1215. IEEE, 2021

2021
[272]

Computational politeness in natural language processing: A survey.ACM Computing Surveys, 56(9):1–42, 2024

Priyanshu Priya, Mauajama Firdaus, and Asif Ekbal. Computational politeness in natural language processing: A survey.ACM Computing Surveys, 56(9):1–42, 2024

2024
[273]

A survey of knowledge enhanced pre-trained language models.IEEE Transactions on Knowledge and Data Engineering, 36(4):1413–1430, 2023

Linmei Hu, Zeyi Liu, Ziwang Zhao, Lei Hou, Liqiang Nie, and Juanzi Li. A survey of knowledge enhanced pre-trained language models.IEEE Transactions on Knowledge and Data Engineering, 36(4):1413–1430, 2023

2023
[274]

Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human langua...

2019
[275]

Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, pages 1–13, 2019

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, pages 1–13, 2019. Manuscript submitted to ACM 34 C. ...

1907 arXiv
[276]

Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32:1–11, 2019

Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32:1–11, 2019

2019
[277]

Language models are unsupervised multitask learners

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019

2019
[278]

Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

1901
[279]

Gpt-4 technical report.arXiv preprint arXiv:2303.08774, pages 1–100, 2023

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774, pages 1–100, 2023

2023 arXiv
[280]

Kg-bart: Knowledge graph-augmented bart for generative commonsense reasoning

Ye Liu, Yao Wan, Lifang He, Hao Peng, and S Yu Philip. Kg-bart: Knowledge graph-augmented bart for generative commonsense reasoning. In Proceedings of the AAAI conference on artificial intelligence, number 7 in 35, pages 6418–6425, 2021

2021
[281]

Manning, and Jure Leskovec

Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, and Jure Leskovec. Greaselm: Graph reasoning enhanced language models. InICLR, pages 1–16. OpenReview.net, 2022

2022
[282]

JAKET: joint pre-training of knowledge graph and language understanding

Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. JAKET: joint pre-training of knowledge graph and language understanding. In AAAI, pages 11630–11638. AAAI Press, 2022

2022
[283]

Goproteingnn: Leveraging protein knowledge graphs for protein representation learning

Dan Kalifa, Uriel Singer, and Kira Radinsky. Goproteingnn: Leveraging protein knowledge graphs for protein representation learning. In Meeyoung Cha, Chanyoung Park, Noseong Park, Carl Yang, Senjuti Basu Roy, Jessie Li, Jaap Kamps, Kijung Shin, Bryan Hooi, and Lifang He, editor...

2025
[284]

Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward

Luyang Huang, Lingfei Wu, and Lu Wang. Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward. InACL, pages 5094–5107. Association for Computational Linguistics, 2020

2020
[285]

Few-shot knowledge graph-to-text generation with pretrained language models

Junyi Li, Tianyi Tang, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, and Ji-Rong Wen. Few-shot knowledge graph-to-text generation with pretrained language models. InACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 ofFindings of ACL, pages 1558–1568. Association for Computatio...

2021
[286]

Cognlg: Cognitive graph for kg-to-text generation

Peichao Lai, Feiyang Ye, Yanggeng Fu, Zhiwei Chen, Yingjie Wu, Yilei Wang, and Victor Chang. Cognlg: Cognitive graph for kg-to-text generation. Expert Syst. J. Knowl. Eng., 41(1):1–17, 2024

2024
[287]

Biomedical knowledge graph embedding with householder projection (student abstract)

Sensen Zhang, Xun Liang, Simin Niu, Xuan Zhang, Chen Feng, and Yuefeng Ma. Biomedical knowledge graph embedding with householder projection (student abstract). InAAAI, pages 23707–23708. AAAI Press, 2024

2024
[288]

MKG-FENN: A multimodal knowledge graph fused end-to-end neural network for accurate drug-drug interaction prediction

Di Wu, Wu Sun, Yi He, Zhong Chen, and Xin Luo. MKG-FENN: A multimodal knowledge graph fused end-to-end neural network for accurate drug-drug interaction prediction. InAAAI, pages 10216–10224. AAAI Press, 2024

2024
[289]

Muffin: multi-scale feature fusion for drug–drug interaction prediction.Bioinformatics, 37(17):2651–2658, 2021

Yujie Chen, Tengfei Ma, Xixi Yang, Jianmin Wang, Bosheng Song, and Xiangxiang Zeng. Muffin: multi-scale feature fusion for drug–drug interaction prediction.Bioinformatics, 37(17):2651–2658, 2021

2021
[290]

An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network.Computers in Biology and Medicine, 169:107900, 2024

Siqi Chen, Ivan Semenov, Fengyun Zhang, Yang Yang, Jie Geng, Xuequan Feng, Qinghua Meng, and Kaiyou Lei. An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network.Computers in Biology and Medicine, 169:107900, 2024

2024
[291]

Hetddi: a pre-trained heterogeneous graph neural network model for drug–drug interaction prediction.Briefings in Bioinformatics, 24(6):bbad385, 2023

Zhe Li, Xinyi Tu, Yuping Chen, and Wenbin Lin. Hetddi: a pre-trained heterogeneous graph neural network model for drug–drug interaction prediction.Briefings in Bioinformatics, 24(6):bbad385, 2023

2023
[292]

Zhong-Hao Ren, Zhu-Hong You, Chang-Qing Yu, Li-Ping Li, Yong-Jian Guan, Lu-Xiang Guo, and Jie Pan. A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks.Briefings in bioinformatics,...

2022
[293]

Lagat: link-aware graph attention network for drug-drug interaction prediction.Bioinform., 38(24):5406–5412, 2022

Yue Hong, Pengyu Luo, Shuting Jin, and Xiangrong Liu. Lagat: link-aware graph attention network for drug-drug interaction prediction.Bioinform., 38(24):5406–5412, 2022

2022
[294]

Medical-knowledge-based graph neural network for medication combination prediction.IEEE Transactions on Neural Networks and Learning Systems, pages 1–12, 2023

Chao Gao, Shu Yin, Haiqiang Wang, Zhen Wang, Zhanwei Du, and Xuelong Li. Medical-knowledge-based graph neural network for medication combination prediction.IEEE Transactions on Neural Networks and Learning Systems, pages 1–12, 2023

2023
[295]

A disease-drug interaction prediction framework based on knowledge graph and graph contrastive learning for recommendation system

Zhongwei An, Jiajie Xing, and Xianguo Zhang. A disease-drug interaction prediction framework based on knowledge graph and graph contrastive learning for recommendation system. In2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 6260–6267. IEEE, 2024

2024
[296]

KGNN: knowledge graph neural network for drug-drug interaction prediction

Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. KGNN: knowledge graph neural network for drug-drug interaction prediction. InIJCAI, pages 2739–2745. ijcai.org, 2020

2020
[297]

Glass, Jimeng Sun, and Cao Xiao

Yue Yu, Kexin Huang, Chao Zhang, Lucas M. Glass, Jimeng Sun, and Cao Xiao. Sumgnn: multi-typed drug interaction prediction via efficient knowledge graph summarization.Bioinform., 37(18):2988–2995, 2021

2021
[298]

Unifying large language models and knowledge graphs: A roadmap

Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599, 2024

2024
[299]

ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation.CoRR, abs/2107.02137, 2021

Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, Weixin Liu, Zhihua Wu, Weibao Gong, Jianzhong Liang, Zhizhou Shang, Peng Sun, Wei Liu, Xuan Ouyang, Dianhai Yu, Hao Tian, Hua Wu, and Haifeng Wang. ERNIE ...

2021 arXiv
[300]

Dkplm: decomposable knowledge-enhanced pre-trained language model for natural language understanding

Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He, and Jun Huang. Dkplm: decomposable knowledge-enhanced pre-trained language model for natural language understanding. InProceedings of the AAAI Conference on Artificial Intelligence, number 10 in 36,...

2022

Pith tools

Reviewed July 14, 2026 · model on record in the stance chip above.

[1] [1]

Knowledge graphs: Opportunities and challenges.Artificial Intelligence Review, 56(11):13071–13102, 2023

Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne. Knowledge graphs: Opportunities and challenges.Artificial Intelligence Review, 56(11):13071–13102, 2023

2023

[2] [2]

Bolt defect classification algorithm based on knowledge graph and feature fusion.Energy Reports, 8:856–863, 2022

Yinghui Kong, Xu Liu, Zhenbing Zhao, Dongxia Zhang, and Jikun Duan. Bolt defect classification algorithm based on knowledge graph and feature fusion.Energy Reports, 8:856–863, 2022

2022

[3] [3]

A survey of techniques for constructing chinese knowledge graphs and their applications

Tianxing Wu, Guilin Qi, Cheng Li, and Meng Wang. A survey of techniques for constructing chinese knowledge graphs and their applications. Sustainability, 10(9):3245, 2018

2018

[4] [4]

A survey on cybersecurity knowledge graph construction.Computers & Security, 136:103524, 2024

Xiaojuan Zhao, Rong Jiang, Yue Han, Aiping Li, and Zhichao Peng. A survey on cybersecurity knowledge graph construction.Computers & Security, 136:103524, 2024

2024

[5] [5]

Domain-specific knowledge graphs: A survey.Journal of Network and Computer Applications, 185:103076, 2021

Bilal Abu-Salih. Domain-specific knowledge graphs: A survey.Journal of Network and Computer Applications, 185:103076, 2021

2021

[6] [6]

A decade of scholarly research on open knowledge graphs

Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, and Mohamed Ben Aouicha. A decade of scholarly research on open knowledge graphs. InLREC/COLING, pages 438–448. ELRA and ICCL, 2024

2024

[7] [7]

Generative knowledge graph construction: A review

Hongbin Ye, Ningyu Zhang, Hui Chen, and Huajun Chen. Generative knowledge graph construction: A review. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors,Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1–17, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics

2022

[8] [8]

Multi-modal knowledge graph construction and application: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(2):715–735, 2022

Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, and Nicholas Jing Yuan. Multi-modal knowledge graph construction and application: A survey.IEEE Transactions on Knowledge and Data Engineering, 36(2):715–735, 2022

2022