arXiv AI
LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction
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Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning
EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
arXiv AI
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When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
arXiv:2608.31118v1 Announce Type: new Abstract: The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled eval...
arXiv AI
Jul 28
Retrieval-Augmented Generation of Ontologies from Relational Databases
arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.
arXiv AI
Sep 10
OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?
arXiv:2609.08174v1 Announce Type: new Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs a...
arXiv Computation and Language
Sep 23
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv AI
Aug 25
Toward Effective and Reliable LLM Agents via Dynamic Ontology
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...