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REVIEW 5 major objections 5 minor 29 references

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

T0 review · 5 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read OntoExtend uses retrieval-grounded LLM generation to extend existing ontologies, passing all 39 competency questions with under 2% superfluous elements and few structural defects.

desk verdict A solid engineering framework for CQ-driven ontology extension, but the 100% verification and <2% superfluous numbers are undercut by post-hoc evaluation; the paper deserves peer review with major revisions. read the letter →

arxiv 2607.17963 v1 pith:PGW2NRKD submitted 2026-07-20 cs.AI

Anna Sofia Lippolis , Mohammad Javad Saeedizade , Stefan Schmid , Simon Blattner , Robin Keskisärkkä , Aldo Gangemi , Eva Blomqvist , Andrea Giovanni Nuzzolese This is my paper
classification cs.AI
keywords ontologyextensionlargelanguagemodelsretrieval-augmentedgenerationcompetencyquestionsevaluationSPARQLverificationreuseSHACL
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

OntoExtend is a framework for extending existing ontologies with the help of large language models (LLMs), driven by new requirement questions called competency questions (CQs). It retrieves the most relevant classes, properties, and axioms from the ontology, feeds them to an LLM as grounded context, and generates a small fragment of new axioms that extend the ontology to answer the CQ. The paper evaluates this on 39 CQs across two real ontologies, finding that fragments are syntactically valid, introduce almost no modelling pitfalls, contain fewer than 2% superfluous elements, and all CQs pass a SPARQL-based functional test. Ontology engineers rated the outputs as needing only minor to moderate revision, supporting the claim that OntoExtend can act as a practical drafting assistant for ontology maintenance.

What carries the argument

The central mechanism is retrieval-augmented generation (RAG) over the ontology itself: each class, property, and axiom is serialized as a pipe-delimited text element, embedded with a sentence model, and stored in a vector index; a new competency question is embedded and the top-k most similar elements are retrieved and injected into the LLM prompt as Turtle snippets. This grounding step is what keeps the generated axioms consistent with the existing ontology's terminology and modelling patterns, and is what the authors credit for the low structural defect rate and the near-absence of superfluous elements.

What would settle it

Have a team uninvolved in the design of a test ontology write the CQs and, independently, have annotators check whether the generated fragments correctly model the intended meaning without relying on SPARQL-answerability. If a substantial fraction of fragments pass the paper's verification but are judged semantically wrong by independent annotators, the central claim of practical automation is undermined.

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Extended reading notes

Core claim

The authors claim that ontology extension — enriching an existing ontology to satisfy new requirements — can be largely automated without sacrificing quality, if the LLM's generation is grounded in the specific ontology elements that are relevant to each requirement. Their pipeline retrieves top-k relevant ontology entities via dense vector embeddings of element descriptions, injects the corresponding Turtle snippets into the prompt, and generates a self-contained axiom fragment. On two real-world cases, the fragments were structurally clean, verified against all 39 competency questions, added under 2% superfluous elements, and received positive human ratings, leading the authors to conclude

Load-bearing premise

The evaluation assumes that competency questions formed by deleting classes from an ontology and asking what those classes were meant to represent faithfully capture real-world extension needs, and that a fragment is correct if a SPARQL query can be written to retrieve an answer for the CQ.

Editorial extensions

If this is right

  • Mature ontologies can be extended incrementally as new requirements arise, with LLM-generated drafts that engineers review instead of authoring from scratch.
  • The retrieval step makes the approach scalable to ontologies far larger than an LLM's context window, since only a compact relevant subset is sent to the model.
  • The framework is portable across domains and modelling profiles via configurable prompt templates (e.g., OWL restrictions vs. SHACL shapes), demonstrated on two different real-world settings.
  • All generated fragments passed the functional CQ-verification test and added fewer than 2% superfluous elements, a large improvement over reported rates in from-scratch LLM ontology generation.
  • LLM performance on extension appears sensitive to CQ specificity, suggesting that quality of requirements is a key downstream factor.

Reading between the lines

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

  • Because the same team crafted the removed classes, the CQs, and the verification SPARQL queries, the 100% CQ-verification rate may overestimate semantic correctness; an independent evaluation with third-party CQs would test this.
  • The retrieval step is text-based (serialized labels, comments, and axioms); a graph-structural retriever that exploits relational neighbours might retrieve different elements for CQs that require deep inference chains.
  • Enabling the optional re-indexing of generated fragments (disabled in this evaluation) could improve cross-fragment consistency, but would also make errors propagate; this is a testable design choice.
  • The authors' interpretation of LLM behaviour as a proxy for CQ quality suggests a practical diagnostic: poorly performing extensions flag under-specified requirements, which could be used to prompt better requirement elicitation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper introduces OntoExtend, a retrieval-augmented generation (RAG) framework for extending existing ontologies with LLMs, driven by competency questions (CQs). The pipeline consists of an Ontology Retriever that indexes and retrieves relevant elements from input ontologies, an Ontology Extender that prompts an LLM to generate Turtle fragments, and an Ontology Integrator that merges fragments back into the source ontologies. The framework is evaluated on 39 CQs from two real-world settings: four modules of the public EU-project ontology Onto-DESIDE and an industrial Bosch ontology. The reported results indicate few structural pitfalls, 100% CQ verification, fewer than 2% superfluous elements, and engineer ratings of 4.5–4.9 for the industry setting and 2.9–3.7 for the EU setting. The paper claims OntoExtend provides a practically useful drafting-assistant level of automation for real-world ontology extension.

Significance. If the central claims hold, OntoExtend would be a meaningful step toward scalable, requirement-driven ontology extension with LLMs, particularly in its use of retrieval to ground generation in existing ontology elements. The paper has concrete strengths: it implements and releases a working framework, applies structural checks (Turtle syntax, OOPS!, Pellet consistency), and reports a human evaluation with inter-annotator agreement. These are valuable beyond the specific results. However, the quantitative support for 'practically useful automation' rests on two metrics—CQ verification and superfluous-element rate—that are defined circularly with respect to the authors' own post-hoc SPARQL queries, and the evaluation lacks a within-experiment baseline and uses CQs reverse-engineered from the target ontologies. The significance is therefore conditional: the framework is plausible and the industrial survey is encouraging, but the central evidence is not yet independently probative.

major comments (5)
  1. [Section 4.1, Section 5.2, Section 6.2] CQ verification is post-hoc. The definition states that a CQ is considered modelled if the authors can 'attempt to formulate a SPARQL query that retrieves an answer for that CQ' after seeing the generated fragment. The same team writes the CQs, the expected answers, and the verification queries, and the queries are not pre-registered. A semantically wrong fragment—e.g., one using an inverse property, a mis-scoped class, or an overly broad domain—can still be queried successfully. Therefore the reported 100% verification rate in Table 4 does not establish that the intended model was recovered. This is load-bearing because the abstract and Section 7 rely on this result.
  2. [Section 4.1, Section 6.2] The superfluous-element metric is not independent of CQ verification. By definition, an element is superfluous if it is not mentioned in the verification SPARQL and is not a subclass/subproperty of a query component. Since the verification SPARQL is written after the fragment is generated, the <2% superfluous rate is partly an artifact of the authors' ability to write queries that fit the generated fragment. The comparison to the 'around 30%' in [16] is also not a controlled baseline: it compares different ontologies, different prompts, and different tasks. A within-experiment baseline is needed to support the claim that retrieval reduces superfluous elements.
  3. [Section 4, Dataset Creation] The benchmark is constructed by removing classes and their related properties from the target ontologies and then asking what these removed elements were 'intended to represent or accomplish within the original ontology.' This assumes that removal-based CQs are representative of real extension requirements, and it makes the expected answers knowable to the authors by construction. Because the same group formulated the CQs, the answers, and the verification queries, the risk of circularity is high. Independent gold CQs, blind evaluation, or at least a preregistered query/answer set would be needed to treat the functional evaluation as evidence of correct modelling.
  4. [Section 6.3, Section 7, Section 8] The paper's central claim overstates the survey results. Section 6.3 states that 'the evaluators with high agreement concluded that the generated fragments require moderate revision before they can be safely integrated,' and Table 5 shows EU-project completeness means of 2.94–3.11, which on the paper's own scale corresponds to 'moderate changes' (11–25% revision), not 'minor' or 'light.' Yet Section 7 claims OntoExtend 'generates a complete extension module that users typically only need to review and lightly edit,' and the abstract similarly says 'minor to moderate revision.' The EU-project results support only a drafting-assistant claim with nontrivial post-editing effort in open-CQ settings; the 'lightly edit' formulation is not supported by the reported data.
  5. [Footnote 1 vs Section 7, Limitations] There is a direct contradiction in the availability statement. Footnote 1 says 'The code and the experiment data (ontologies and CQs), along with examples ... are available at https://github.com/...' while Section 7 states 'for the EU-Project ontology, the ontologies have been publicly released, but the material concerning CQs and their coverage is not publicly available' and that the industry ontology 'was not disseminated in public code or data repositories.' This inconsistency blocks independent replication and must be resolved by making the actual artifacts available or clearly specifying what is and is not released.
minor comments (5)
  1. [Section 3.1] Typo: 'For each entity. It constructs an OntologyElement record' should be 'For each entity, it constructs...'.
  2. [Section 4, Parameter tuning] 'cross cross-checking' is a duplication; should be 'cross-checking'.
  3. [Table 4] The OOPS! column header 'P2, P4 & P8' is not defined in the caption; it should state that these are minor pitfall types and clarify whether the counts are per-fragment or per-use-case.
  4. [Section 3.1] The 'Element to be embedded' example in Figure 2 is described in text but the figure itself is not referenced in a way that clarifies the pipe-delimited serialization; consider showing a full example inline.
  5. [Section 4.1] The survey rating scale for Completeness is ordinal but means are reported; no standard deviation or per-evaluator distribution is given, making it hard to assess the spread behind the Fleiss P_o values.

Circularity Check

2 steps flagged · score 6.0 of 10

CQ verification and superfluous-element metrics are post-hoc: the verification SPARQL is written after generation, so the 100% pass rate and <2% superfluous count are partly built into the definitions.

  1. self definitional [Section 4.1 (Functional Metrics) and Section 6.2]
    "CQ verification evaluates whether a given CQ is actually represented in the ontology by attempting to formulate a SPARQL query that retrieves an answer for that CQ. If no such SPARQL query can be written to obtain an answer, the CQ is considered not modelled in the ontology."

    The query is written by the same authors after the LLM has generated the fragment. Any non-empty fragment supplies named classes/properties that can be mentioned in a query, so a query can almost always be formulated unless the fragment is empty. Therefore the reported 100% CQ-verification rate ('all CQs in both use cases are correctly modelled', Section 6.2) is guaranteed by the metric's definition rather than by independent evidence that the intended removed element was recovered.

  2. self definitional [Section 4.1 (Superfluous elements) and Section 6.2]
    "a superfluous element is a named class or property that: 1) is not mentioned in the verification SPARQL used to in CQ verification, and 2) is not connected to any component appearing in that query by a subClassOf or subPropertyOf relation."

    Because the verification SPARQL is authored after seeing the generated fragment, the authors can mention any generated element in the query (or connect it by a subclass relation) and thus exclude it from the superfluous count. The reported '<2% superfluous elements' is consequently not independent of the post-hoc verification query; it partly measures whether the evaluators chose to reuse the generated names in their own query.

full rationale

The paper is not a derivation with fitted constants; it is an empirical evaluation of a retrieval-augmented LLM pipeline. The retrieval configuration is tuned on a held-out dev set, and the human survey provides independent evidence (industry high, EU moderate), so the framework itself is not circular. However, the two headline quantitative claims are weakened by definition: CQ verification is defined as the authors' ability to write a post-hoc SPARQL query, and a non-empty generated fragment almost always allows such a query; superfluous elements are defined relative to that same query. Thus the 100% verification rate and <2% superfluous count are partly artifacts of the metric, not independent measurements. The self-citations to [16] (model selection, prompt seed, comparison baseline) are transparent and not load-bearing: both models are evaluated directly and the comparison reports [16]'s original definition as well. The dataset construction (removing target elements and writing CQs from them) is a reasonable gold-standard protocol, but combined with post-hoc verification it closes a self-referential loop; Section 7's data-leakage caveat and the footnote/GitHub availability inconsistency further limit external replication. Net: partial circularity in the functional evaluation, with independent human evaluation preventing a higher score.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The paper is empirical, so there are no mathematical axioms; the ledger lists the domain and evaluation assumptions that the central claim depends on. The two free parameters (top-k and embedding-selection weights) are small in number, but the dependence on author-constructed CQs and self-defined CQ verification is the main burden.

free parameters (2)
  • top-k retrieval size = 20 (default)
    Number of ontology elements injected into the LLM prompt; affects grounding and output quality. Set as default without reported sensitivity analysis (Section 3.1).
  • Embedding-selection weights M_w = 0.7*P@3 + 0.3*P@20
    Hand-chosen weights used to select text-embedding-ada-002; a different weighting could select a different embedder and change retrieval quality (Section 4, Parameter tuning).
assumptions (6)
  • domain assumption Semantic relevance of ontology elements to a CQ is captured by cosine similarity of pipe-delimited text embeddings.
    Section 3.1; if retrieval misses relevant elements, the LLM has no grounding for the extension.
  • domain assumption A CQ is adequately modelled whenever a SPARQL query can be written that retrieves an answer after integration.
    Section 4.1; an existence-of-query test does not check whether the modelling matches the intended meaning.
  • ad hoc to paper Removal-based CQs (created by deleting elements and asking what they represented) are representative of real ontology-extension requirements.
    Section 4 (Dataset Creation); the benchmark is constructed from the answer, so it may be easier or different in kind from unanticipated real requirements.
  • domain assumption The evaluation LLMs have not memorized the test ontologies, or the leakage is negligible.
    Section 7 (Limitations) admits the public Onto-DESIDE ontology may have been seen during pre-training; if leakage occurred, scores are inflated.
  • domain assumption Six ontology engineers (three per use case) provide a reliable assessment of fragment quality.
    Section 5.3; small convenience sample, no blinding, and no independent gold standard.
  • domain assumption OOPS! and Pellet detect all relevant structural defects introduced by the generated fragments.
    Section 5.1; automated tools may miss semantic errors that engineers later found (e.g., o1-preview's syntactically incorrect axioms 'not shown by the tools').

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Cite this review

Pith. "Pith review of OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs." pith.science (2026). https://pith.science/paper/PGW2NRKD

@misc{pith2026260717963,
  author       = {Pith},
  title        = {Pith review of: OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PGW2NRKD}},
  note         = {Machine review of arXiv:2607.17963}
}
read the original abstract

Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of LLM outputs. This paper introduces OntoExtend, a requirements-driven framework for ontology extension with LLMs. It uses retrieval-augmented generation (RAG) over relevant input ontologies and requirements in the form of competency questions to propose grounded extensions. We evaluate OntoExtend on 39 CQs from two use cases: a public EU-project ontology, Onto-DESIDE, and an industrial ontology from Bosch. The generated fragments show few structural issues, satisfy all functional evaluation tests, and are rated by ontology engineers as requiring minor to moderate revision before integration. These results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real world scenarios, while remaining sensitive to CQ specificity and modelling profile.

Figures

Figures reproduced from arXiv: 2607.17963 by the authors.

Figure 1
Figure 1. Overview of Ontoextend: 1) The Ontology Retriever extracts the relevant ontology elements of the input ontologies given a new competency question. 2) The Ontology Extender uses the retrieved ontology elements together with the competency question to prompt an LLM to generate missing ontology fragments. 3) The Ontology Integrator integrates the fragments into the extended ontologies. of competency questions (CQs), i.… view at source ↗
Figure 2
Figure 2. Example of the element to be embedded. 3. The OntoExtend framework The proposed system implements a retrieval-based pipeline for ontology extension, or￾ganised into three principal subsystems: the Ontology Retriever, the Ontology Extender, and the Ontology Integrator, illustrated in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Evaluation workflow: input ontologies, the generated fragments, and the extended ontologies are fed to different evaluation metrics to create the final report. the fragment is syntactically correct but semantically incorrect, for instance due to an inappropriate taxonomy. A rating of 4 corresponds to a fragment that is both syntactically and semantically correct, but does not fully satisfy the intent of the competen… view at source ↗

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