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

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

arXiv · 2509.04696

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

Samira Khorshidi·Azadeh Nikfarjam·Suprita Shankar·Yisi Sang·Yash Govind·Hyun Jang·Ali Kasgari·Alexis McClimans·Mohamed Soliman·Vishnu Konda·Ahmed Fakhry·Xiaoguang Qi

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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.

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Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar, Yisi Sang, Yash Govind, Hyun Jang, Ali Kasgari, Alexis McClimans, Mohamed Soliman, Vishnu Konda, Ahmed Fakhry, Xiaoguang Qi. 2025-09-04. ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs. https://arxiv.org/abs/2509.04696

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