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

arXiv: https://arxiv.org/abs/2509.04696 · HTML: https://mlsanity.com/paper/2509.04696/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System](https://mlsanity.com/paper/2412.20005/) (2024)
- [Integrating Large Language Models and Knowledge Graphs for Extraction and Validation of Textual Test Data](https://mlsanity.com/paper/2408.01700/) (2024)
- [Growing and Serving Large Open-domain Knowledge Graphs](https://mlsanity.com/paper/2305.09464/) (2023)
- [Open Domain Knowledge Extraction for Knowledge Graphs](https://mlsanity.com/paper/2312.09424/) (2023)

## Built on by (most cited first, 4 of 4)

- [LLM-empowered knowledge graph construction: A survey](https://mlsanity.com/paper/2510.20345/) (2025)
- [Ontology-to-tools compilation for executable semantic constraint enforcement in LLM agents](https://mlsanity.com/paper/2602.03439/) (2026)
- [Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls](https://mlsanity.com/paper/2605.03147/) (2026)
- [From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development](https://mlsanity.com/paper/2609.11493/) (2026)

## Abstract (arXiv)

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.