arXiv AI

LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction

arXiv AI

Sep 10

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.

By Chang Zong, Jinyu Chen, Sicheng Lv, Si-tu Xue, Huilin Zheng, Jian Wan, Lei Zhang

llms rag nlp

arXiv AI

Sep 1

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...

By Hamed Babaei Giglou, S\"oren Auer, Jennifer D'Souza

llms rag

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.

By Nadeen Fathallah, Mojtaba Nayyeri, Athish A Yogi, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab

llms rag

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...

By Xiao Yu Cindy Zhang, Wyeth Wasserman, Jian Zhu

llms rag fine-tuning benchmarks

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...

By David Torres-Moreno, Jorge Hermosillo-Valadez

llms agents nlp

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,...

By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yuanning Cui, Lingbing Guo, Wei Hu

llms agents

arXiv AI

Sep 2

Do General NLP Embeddings Capture Ontological Reasoning?

The paper introduces AVA, a framework that tests whether general NLP embeddings can differentiate logic-sensitive relational semantics in ontologies and knowledge graphs. AVA uses 171,007 contrastive triplets from 163 ontologies, each containing an ontology statement, a paraphrase, and a hard negative with contradictory meaning. Evaluation of over 25 embedding models shows significant limitations, with the best model achieving only 0.739 triplet accuracy and 0.135 for hard negatives; fine‑tuning helps but does not transfer well to downstream Semantic Web tasks.

By Hamed Babaei Giglou, Jennifer D'Souza, S\"oren Auer

rag nlp fine-tuning benchmarks safety

arXiv Machine Learning

Sep 10

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are...

By Xiaohua Lu, Liubov Tupikina, Mehwish Alam

benchmarks

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 3

OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models

arXiv:2607. 01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices.

By Hamed Babaei Giglou, Jennifer D'Souza, Andrei Aioanei, Nandana Mihindukulasooriya, S\"oren Auer

llms benchmarks

arXiv AI

Jul 21

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

arXiv:2607. 16201v1 Announce Type: new Abstract: Ontology engineering remains a critical bottleneck in knowledge-intensive AI systems.

By Sergei Sergienko

llms benchmarks

arXiv AI

Jun 29

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.

By Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang

nlp

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