# AutoOntic: Automatic Ontology Induction from Text for Knowledge Graph Construction

**Authors:** Alla Chepurova, Aydar Bulatov, Mikhail Burtsev, Yuri Kuratov  
**Published:** 11 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.15467) · [PDF](https://arxiv.org/pdf/2611.15467) · [Code](https://github.com/screemix/AutoOntic) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.15467.html)

## Key numbers

- **90%**: over 90% of AutoOntic's induced endpoint types are semantically aligned with the reference classes across two…
- **88%**: knowledge graphs organized using an ontology built by AutoOntic achieve 88% retrieval accuracy

## 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 with no type hierarchy and no domain/range constraints. We present AutoOntic, a pipeline that automatically constructs an ontology from text by canonicalizing open-vocabulary entity types and relations, inducing a hierarchy over the canonicalized types, and mining relation-level domain and range constraints. Its hierarchy induction procedure prioritizes types using corpus-derived relation-argument profiles and places them through local, LLM-adjudicated parent/child checks, allowing newly inserted types to trigger reassignment of existing parent-child links. The resulting hierarchy constrains entity deduplication and supports the induction of relation-level domain/range constraints. For intrinsic evaluation, we introduce a reference-based protocol that classifies induced ontology types as exact, broader, narrower, or unrelated to reference domain and range classes. On the Wikidata-TekGen and DBpedia-WebNLG subsets of Text2KGBench, over 90% of AutoOntic's induced endpoint types are semantically aligned with the reference classes across two LLM backbones: about half match exactly, while most remaining differences concern granularity. On MINE, knowledge graphs organized using an ontology built by AutoOntic achieve 88% retrieval accuracy. AutoOntic is available at https://github.com/screemix/AutoOntic.

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