Problem
Research questions and friction points this paper is trying to address.
Ontology Induction
Domain Documents
Corpus Grounding
Vocabulary Consistency
Axiom-Level Expressivity
GrOIL: Graph-Grounded Domain Ontology Induction with Constrained LLM Mediation
🤖 AI Summary
提出了一种基于图的七阶段流程GrOIL，利用受限的大语言模型中介从领域文档构建完整可审计的OWL本体，解决了现有系统无法同时满足语料基础、词汇一致性等问题。
📝 Abstract
Constructing formal ontologies from domain documents requires simultaneously enforcing corpus grounding, vocabulary consistency, axiom-level expressivity, and end-to-end provenance, a combination no existing automatic system delivers. We present a seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step. Documents are first encoded as Unified Discourse-Hypergraphs (UDH) capturing entity participation and discourse dependencies; subsequent stages transform this graph evidence into a class hierarchy, typed object and datatype properties, and restriction axioms, with Large Language Model (LLM) usage restricted to narrow, graph-grounded mediation tasks. A paired Assertional Box (ABox) population procedure grounds named individuals in the induced TBox, enabling SPARQL-based functional evaluation. Every emitted term carries a full decision chain from raw source passages through each pipeline stage, making the TBox directly auditable and suitable for targeted human refinement. Evaluated on the life insurance domain using two established benchmarks and a new 100-contract corpus spanning ten product types, our pipeline achieves strong results across all evaluation dimensions, outperforming direct and multi-agent LLM baselines on competency-question (CQ) coverage (0.85 vs. 0.63 and 0.62 on one term-life contract; 0.77 vs. 0.40 and 0.44 on another contract), while also attaining high keyphrase coverage comparable to a manually-constructed reference ontology and strong performance on structured gap-and-overlap reasoning, all without any manual TBox engineering. Ontology growth analysis provides evidence consistent with vocabulary saturation at scale, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph-Grounded
Unified Discourse-Hypergraphs (UDH)
Large Language Model (LLM) Mediation
Web Ontology Language (OWL) TBox
Auditable Decision Chain
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