ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs

Ahmed Fakhry; Alexis McClimans; Ali Kasgari; Azadeh Nikfarjam; Hyun Jang; Mohamed Soliman; Samira Khorshidi; Suprita Shankar; Vishnu Konda; Xiaoguang Qi

REVIEW 4 major objections 4 minor 14 references

ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper reports a production LLM knowledge-extraction pipeline that ingested 19 million facts from Wikipedia at 98.8% precision.

desk verdict A coherent production system report from Apple's KG team, but the headline metrics rest on internal audits with no released protocol—treat 98.8% precision as a claim, not a verified result. read the letter →

arxiv 2509.04696 v1 pith:AJECSOH4 submitted 2025-09-04 cs.CL cs.AI

Samira Khorshidi , Azadeh Nikfarjam , Suprita Shankar , Yisi Sang , Yash Govind , Hyun Jang , Ali Kasgari , Alexis McClimans

show 4 more authors

Mohamed Soliman Vishnu Konda Ahmed Fakhry Xiaoguang Qi

This is my paper · ORCID

classification cs.CLcs.AI

keywords knowledgegraphconstructionLLM-basedextractionontology-guidedpromptingfactgroundingcorroborationandrankingopen-domainWikipediaproductiondeployment

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

ODKE+ is a production knowledge-graph pipeline that uses large language models to pull facts out of web pages and still keeps precision high enough to trust. The paper reports that the system ingested more than 19 million facts across 195 predicates from over 9 million Wikipedia pages at 98.8% audited precision, with grounding alone cutting hallucinated extractions by 35%. The core design is ontology-guided prompting: each entity type gets a compact schema snippet listing valid predicates, qualifiers, and units, so the LLM produces schema-consistent output without per-predicate hand-built rules. A second, lightweight LLM then checks each candidate fact against its source text, and a corroborator normalizes, aggregates, and ranks the survivors before ingestion. The result, if the numbers hold, turns LLM extraction from a research demonstration into a viable primary channel for keeping large ontologies fresh.

What carries the argument

The central mechanism is ontology snippet generation: an offline pipeline inspects the KG, the ontology, and Wikidata, ranks predicates per entity type, enriches them with descriptions, domains, ranges, qualifiers, cardinalities, and normalization configs, then serializes this into a compact text fragment placed inside the LLM prompt. This keeps extractions aligned with schema constraints without manual per-predicate rules. The second load-bearing component is the Grounder, a separate LLM that receives the source context and an assertion of the extracted triple and answers True or False; only affirmative verdicts proceed to the Corroborator, which converts values into canonical units and for

What would settle it

Take a random, stratified sample of the 19 million ingested triples, including long-tail entities and rare predicates, and have a fresh set of annotators independently re-check each triple against its cited provenance without seeing ODKE+'s confidence scores. If measured precision falls well below 98.8%, or if the Grounder's True/False decisions disagree with the re-check at a high rate, the central claim is not supported.

Watch

Extended reading notes

Core claim

ODKE+ claims that ontology-guided prompting plus a lightweight grounding verifier makes LLM-based open-domain fact extraction accurate and fast enough to run continuously in a production knowledge graph. The pipeline detects which entities have missing or stale facts by watching page edits, retrieves the updated Wikipedia content, extracts candidate triples with either pattern rules or an LLM, asks a separate Grounder LLM whether each candidate is explicitly supported by the context, then normalizes, aggregates, and ranks survivors before ingestion. In the reported deployment, this produced 19 million accepted facts at 98.8% precision, reduced hallucinated extractions by 35% compared with un

Load-bearing premise

The load-bearing premise is that the Grounder LLM's True/False verdicts are accurate and that the weekly audit of about 2,000 randomly sampled triples is representative; the paper does not report inter-annotator agreement, the audit sampling protocol, or independent validation of the Grounder's judgments, so all headline precision numbers inherit those assumptions.

Editorial extensions

If this is right

Knowledge graphs can treat LLM extraction as a first-class ingestion source rather than a research prototype, with new predicates added declaratively by updating the ontology snippet instead of writing extractor rules.
Freshness no longer requires full-corpus reprocessing: a streaming mode keyed to page-edit signals can keep high-priority entities current with under-two-hour latency.
A separate lightweight verifier after a generative extractor is a reusable design pattern for reducing hallucinations in structured extraction tasks beyond knowledge graphs.
The reported 35% hallucination reduction and 91% to 98.8% precision lift imply that most LLM errors surviving to the ranking stage are caught by grounding and corroboration, so downstream applications can rely on the ingested triples.
Focusing extraction on frequently stale predicates such as height, population, age, and net worth lets the system spend compute where facts change fastest.

Reading between the lines

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

Editorial inference: the 91% raw-LLM baseline and 35% hallucination reduction are aggregate numbers; reporting how grounding's benefit varies across the 195 predicates would show whether verifier effort should be concentrated on high-volume or high-risk predicate types.
Editorial inference: the reported 69-day head start of Wikipedia over Wikidata suggests a general freshness strategy—monitoring edit streams as a weak signal that knowledge changed—that could transfer to other public sources where the entity-to-source mapping is less deterministic.
Editorial inference: because ontology snippets are derived partly from existing KG usage patterns, the system is likely to reinforce the ontology's current coverage; testing whether ODKE+ can recover predicates or entity types absent from the snippet would clarify whether the approach is truly open-domain.
Editorial inference: the aggregate 98.8% precision may hide variation across long-tail entities and rare predicates, where corroboration has fewer independent sources; mapping that variation would make the production claims more transferable.

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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

ODKE+ is a straightforward production-systems paper: Apple's third iteration of an ontology-guided LLM knowledge-extraction pipeline, deployed since May 2025. The genuinely new pieces are dynamic ontology snippet generation (ranking predicates per entity type and enriching them into prompt templates), a second-LLM grounding filter, and expansion to 195 predicates. The paper is honest that it builds on ODKE v1/v2, and the v1/v2/v3 comparison table is useful.

The system description is clear and the architecture is coherent. The ontology snippet pipeline is a real engineering contribution: instead of throwing the whole schema at the LLM, they generate entity-type-specific prompts with canonical labels, ranges, units, and qualifiers, and refresh them periodically. That addresses a practical cost and token-efficiency problem in open-domain extraction. The Grounder as a lightweight verifier is a reasonable design choice; the corroboration step with H2O AutoML is standard but well explained.

The soft spots are all in the evidence for the headline numbers. The 98.8% precision, 35% reduction in hallucinated extractions, and 91% to 98.8% improvement are based on internal weekly audits of about 2,000 triples. The paper gives no sampling protocol, no inter-annotator agreement, no definition of the third-party KG used for the 48% overlap, no error bars, and no released data or code. The Grounder itself is never validated against human judgment, so the causal claim—grounding reduces hallucinations by 35%, corroboration boosts the rest—is an internal production telemetry number, not a demonstrated result. The stress-test note is right: the 35% could be rejecting easy-to-catch errors, and the 98.8% could be driven more by the corroborator's scoring than by the grounder. That doesn't mean the system doesn't work; it means the preprint doesn't give a reader enough to verify it.

The 48% overlap is also under-specified. And the '50 days earlier' freshness figure is plausible but unmeasured from the outside.

Who is this for? Practitioners building KG ingestion pipelines. The paper is not a scientific breakthrough, but it is a well-described, honest system report with production numbers that are plausible. It deserves peer review—a serious referee should push the authors to add an evaluation section with a clear audit definition, a grounder error analysis, and a per-predicate precision table. Without that, the paper is a demo with impressive-looking metrics; with that, it could be a reference for deploying LLM extraction responsibly. I'd recommend acceptance after major revision, conditional on making the evaluation reproducible.

Referee Report

4 major / 4 minor

Summary. The paper describes ODKE+, a production knowledge-graph extraction pipeline that combines an extraction initiator, evidence retriever, pattern- and LLM-based extractors, an LLM-based grounding verifier, and a corroboration/ranking stage. The authors report deploying the system since May 2025, ingesting 19 million high-confidence facts across 195 predicates, with 98.8% precision, a 35% reduction in hallucinated extractions from grounding, and a 48% overlap with a third-party KG. The central claim is that ontology-guided prompting together with a grounding verifier and corroboration can make LLM-based open-domain knowledge extraction trustworthy at web scale.

Significance. If the reported production metrics are methodologically sound, the paper would be a valuable industrial demonstration that LLM-based knowledge extraction can be deployed with high precision on a large scale. The system's design is internally coherent, and the inclusion of a dynamic ontology-snippet generation pipeline, a lightweight second-LLM grounder, and a corroboration stage are concrete engineering contributions. The paper also presents a favorable comparison of update lag and coverage against prior pipelines. However, the quantitative claims rely almost entirely on internal production monitoring with no released artifacts, no public evaluation protocol, and no detailed error analysis, which limits the paper's current value to the research community as a verifiable scientific contribution.

major comments (4)

[Section 4.1 / Table 2] The headline precision values (98.8%, 91% raw LLM, 95%+ audit accuracy) are presented without any evaluation protocol. Section 3.7 mentions a weekly audit of approximately 2,000 randomly sampled triples, but does not specify the sampling frame, the audit instructions, the number of auditors, or inter-annotator agreement. Without these details, 98.8% is not a statistically defensible claim. The paper should report confidence intervals, sample sizes per metric, and a precise definition of 'correct' used by the auditors.
[Section 3.3.1 / Section 4.1] The Grounder is a load-bearing component: the claim that grounding reduced hallucinated extractions by 35% and that corroboration improved precision from 91% to 98.8% depends on the Grounder's True/False judgments being accurate. However, the paper reports no evaluation of the Grounder against human-labeled ground truth, no precision/recall, and no calibration. A staged evaluation—e.g., measuring precision of (i) raw LLM extractions, (ii) post-grounding, and (iii) post-corroboration on the same audited sample—is necessary to support the attribution. The current wording makes the causal chain unfalsifiable.
[Section 4.2 / Table 2] The '48% overlap with third-party KG' metric is undefined. The paper does not name the third-party KG, describe how overlap was computed (exact triple match? entity+predicate? normalized values?), or specify the evaluation timeframe. Without this, the coverage claim cannot be interpreted or reproduced. The authors should state the KG, matching criterion, and the denominator (e.g., all third-party KG facts for the same entity set? facts present in either KG?).
[Section 4.1] The freshness claim ('facts appear roughly 50 days earlier than legacy KG workflows') lacks a comparison baseline. What is a 'legacy KG workflow' in this context? Is the delay measured from first Wikipedia edit to KG ingestion, or from source publication? The time-lag analysis in Section 3.1 (69 days) uses Wikipedia and Wikidata, but the 50-day improvement is not connected to any measured baseline. Please define both endpoints and the entity/predicate subset used for the comparison.

minor comments (4)

[Section 3, Example] Typos: 'it's hight' should be 'its height'; the example sentence about Taylor Swift is missing a verb ('The Initiator detects recent changes on her Wikipedia page.' should be '...changes to her Wikipedia page.').
[Appendix A / B] The prompts are illustrative but the model names, versions, decoding temperatures, and prompt template versions are not specified. For reproducibility, please identify the LLMs used for extraction and grounding, or state if they are not disclosed due to proprietary constraints.
[Section 3.4] The AutoML-based scoring (H2O) is mentioned but no feature list, training data, or evaluation of the learned ranker is provided. It is unclear how much of the precision gain is due to the AutoML scorer versus the rule-based heuristics.
[Section 3.7] The side-by-side evaluation result ('prefer ODKE+ in roughly two-thirds of cases') is reported without the number of queries, the number of annotators, or the stratification details. This makes the preference claim difficult to assess.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: precision and coverage claims are anchored to external human audits and a third-party KG.

full rationale

ODKE+’s central claims are not derived by construction from their inputs. The precision figures (95%+ weekly audit, 98.8% in Table 2) are defined against a dedicated human KG-quality team that audits ~2,000 randomly sampled triples per week (Section 3.7); this is an external ground truth independent of the pipeline’s own Grounder or Corroborator outputs. The 48% coverage figure is defined against a third-party KG (Section 4.2), providing an external benchmark. The Grounder is a filtering stage, but its judgments are not used as the final precision measure; the paper reports human-audited precision. The Corroborator’s ranking is likewise evaluated post hoc by audits. The only self-citations (Ilyas et al. 2023; Qian et al. 2023) are historical descriptions of ODKE v1/v2 and do not justify the new claims. The ontology-snippet generator uses KG frequency statistics to rank predicates (Section 3.3), which may shape coverage toward existing predicates, but this is a modeling choice, not a circular derivation: it does not define the precision or overlap metrics in terms of those same statistics. The 35% hallucination-reduction claim lacks a specified measurement protocol, which is a verifiability weakness, but the paper does not state that the reduction is computed from the Grounder’s own rejections, so no equation-level reduction can be exhibited. Under the requirement to identify concrete circular steps, none is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities. The assumptions are engineering domain assumptions about signal reliability, mapping, verifier quality, and audit representativeness. The free parameters are the unstated confidence threshold, the learned AutoML ranker weights, and the KG-frequency-derived predicate ranking.

free parameters (3)

High-confidence ingestion threshold = Not specified
The Corroborator keeps only facts above an unspecified confidence threshold; the threshold is a hand-chosen operating point that affects all precision and coverage numbers.
H2O AutoML ranking weights = Not specified
Section 3.4 says the Corroborator uses H2O AutoML to learn weighting functions for scoring facts. These weights are fit to internal data, with no reported train/test separation or feature list.
Ontology snippet predicate ranking = Not specified
Ranked Property Generation in Section 3.3 ranks predicates per entity type by frequency in the KG. This data-derived ranking steers extraction toward already-populated properties and is a free choice when no frequency signal exists.

assumptions (4)

domain assumption Wikipedia page edits are a reliable proxy for new or stale facts in the KG.
Section 3.1 uses the assumption that edits reflect newly available or updated facts, which drives the entire Extraction Initiator. If this proxy is weak, the system misses stale facts and prioritizes noise.
domain assumption Under the controlled Wikipedia domain, entity-to-URL mapping is deterministic and language-specific.
Section 3.2 assumes entity-to-URL mapping is reliable for Wikipedia, reducing evidence retrieval to fetching pages from a crawl index. Any mapping errors propagate to extraction.
domain assumption The Grounder LLM's True/False verdicts are accurate enough to filter hallucinations.
Section 3.3.1 treats the second LLM's grounding judgment as a trustworthy filter. The paper reports no evaluation of the Grounder itself, so all downstream precision claims inherit this assumption.
domain assumption The weekly human audit of approximately 2,000 triples is representative and unbiased.
Section 4.1 uses this audit to assert sustained precision above 95%. No sampling protocol, inter-annotator agreement, or confidence intervals are reported.

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Cite this review

Pith. "Pith review of ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs." pith.science (2026). https://pith.science/paper/AJECSOH4

@misc{pith2026250904696,
  author       = {Pith},
  title        = {Pith review of: ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJECSOH4}},
  note         = {Machine review of arXiv:2509.04696}
}

read the original abstract

Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply both pattern-based rules and ontology-guided prompting for large language models (LLMs), (4) a lightweight Grounder validates extracted facts using a second LLM, and (5) the Corroborator ranks and normalizes candidate facts for ingestion. ODKE+ dynamically generates ontology snippets tailored to each entity type to align extractions with schema constraints, enabling scalable, type-consistent fact extraction across 195 predicates. The system supports batch and streaming modes, processing over 9 million Wikipedia pages and ingesting 19 million high-confidence facts with 98.8% precision. ODKE+ significantly improves coverage over traditional methods, achieving up to 48% overlap with third-party KGs and reducing update lag by 50 days on average. Our deployment demonstrates that LLM-based extraction, grounded in ontological structure and verification workflows, can deliver trustworthiness, production-scale knowledge ingestion with broad real-world applicability. A recording of the system demonstration is included with the submission and is also available at https://youtu.be/UcnE3_GsTWs.

Figures

Figures reproduced from arXiv: 2509.04696 by the authors.

**Figure 1.** ODKE system architecture with main components including evidence retrieval, extraction, corroboration, and KG ingestion(a), and its LLM-based extractor(b) Example. Suppose the entity Taylor Swift’s pages has been recently updated. The Initiator detects recent changes on her Wikipedia page. The Retriever fetches updated content. The LLMbased Extractor identifies "5 ft 11 in" for it’s hight, maps it to the height … view at source ↗

**Figure 2.** Ontology Snippet Generation pipeline. The [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

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URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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