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New research explores ontology-driven retrieval and LLM-based ontology generation

By PulseAugur Editorial · [2 sources] ·

Two research papers explore advanced methods for information retrieval and ontology generation. The first paper, now withdrawn, proposed an ontology-driven approach to personalize information retrieval from XML documents by integrating semantic resources and user profiles. The second paper introduces RIGOR, a retrieval-augmented iterative generation pipeline that uses LLMs to convert relational database schemas into semantically rich OWL2DL ontologies with minimal human intervention. Both papers highlight the potential of semantic web technologies and LLMs to enhance data understanding and accessibility. AI

IMPACT These papers showcase advancements in using LLMs and semantic technologies for more intelligent data retrieval and knowledge representation.

RANK_REASON Two academic papers published on arXiv detailing novel approaches to information retrieval and ontology generation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores ontology-driven retrieval and LLM-based ontology generation

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Two academic papers published on arXiv detailing novel approaches to information retrieval and ontology generation.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ounnaci Iddir, Ahmed-ouamer Rachid, Tai Dinh ·

    Ontology-driven personalized information retrieval for XML documents

    arXiv:2603.21139v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditional information retrieval systems (IRS) often overlook user-specific needs and …

  2. arXiv cs.AI TIER_1 English(EN) · Nadeen Fathallah, Mojtaba Nayyeri, Athish A Yogi, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab ·

    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…

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