Pith. sign in

REVIEW 2 cited by

LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.10146 v1 pith:PJA3NLGK submitted 2024-09-16 cs.CL cs.AI

LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge

classification cs.CL cs.AI
keywords challengellms4ollanguagelargelearningmodelsontologyedition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the potential of Large Language Models (LLMs) in Ontology Learning (OL), a vital process for enhancing the web with structured knowledge to improve interoperability. By leveraging LLMs, the challenge aims to advance understanding and innovation in OL, aligning with the goals of the Semantic Web to create a more intelligent and user-friendly web. In this paper, we give an overview of the 2024 edition of the LLMs4OL challenge and summarize the contributions.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

    cs.AI 2026-05 conditional novelty 6.0

    A 24-dataset benchmark for inducing schema graphs from raw text, plus an auditable LLM-based pipeline that reports the highest scores on the benchmark's four schema-similarity metrics.

  2. Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

    cs.AI 2026-05 reject novelty 4.0

    GOI prompts an LLM to infer a document-class schema, but its headline 'coverage' result mostly measures whether the model echoes the schema it was given.