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New pipeline AutoOntic automates ontology induction for knowledge graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AutoOntic, a new pipeline designed to automatically construct ontologies from text for knowledge graph (KG) construction. This system canonicalizes entity types and relations, induces a hierarchy among them, and mines domain and range constraints for relations. Evaluations on benchmarks like Text2KGBench show that AutoOntic's induced ontology types are semantically aligned with reference classes, and KGs built with its ontologies achieve high retrieval accuracy. AI

IMPACT Automates the creation of structured knowledge for AI systems, potentially improving retrieval and reasoning capabilities.

RANK_REASON The cluster describes a new academic paper detailing a novel method for ontology induction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New pipeline AutoOntic automates ontology induction for knowledge graphs

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The cluster describes a new academic paper detailing a novel method for ontology induction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alla Chepurova, Aydar Bulatov, Mikhail Burtsev, Yuri Kuratov ·

    AutoOntic: Automatic Ontology Induction from Text for Knowledge Graph Construction

    arXiv:2611.15467v1 Announce Type: cross Abstract: Graph-based retrieval-augmented generation depends on the quality of the knowledge graph (KG) it retrieves over, yet schema-free LLM-based extraction pipelines typically produce flat graphs: deduplicated entities and relations wit…

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