Evaluating Different Methods for Semantic Reasoning Over Ontologies

Fernando Zhapa-Camacho, Robert Hoehndorf

Research output: Contribution to conferencePaperpeer-review

Abstract

Reasoning over knowledge bases such as Semantic Web ontologies enables the discovery of new facts from existing knowledge. Knowledge-enhanced machine learning has motivated the development of neuro-symbolic reasoners, which enable faster but approximate computation of new facts or entailments. Neuro-symbolic methods generate vector representations (embeddings) of entities in a knowledge base. We analyze some ontology embedding methods, by implementing them as neuro-symbolic reasoners and evaluating their predictive performance on the datasets and tasks provided by the Semantic Reasoning Evaluation Challenge 2023. We explore two types of embedding methods: graph-based and model-theoretic. Regarding graph-based embeddings, we evaluated the impact of different combinations of graph representation of ontologies with knowledge graph embedding methods. For model-theoretic embeddings, which create models for theories, we evaluate the impact of using several models, enabling approximate semantic entailment.

Original languageEnglish (US)
StatePublished - 2023
Event1st Scholarly QALD Challenge 2023 and 4th SeMantic Answer Type, Relation and Entity Prediction Tasks Challenge, Scholarly QALD 2023 and SemREC 2023 - Athens, Greece
Duration: Nov 6 2023Nov 10 2023

Conference

Conference1st Scholarly QALD Challenge 2023 and 4th SeMantic Answer Type, Relation and Entity Prediction Tasks Challenge, Scholarly QALD 2023 and SemREC 2023
Country/TerritoryGreece
CityAthens
Period11/6/2311/10/23

Keywords

  • approximate entailment
  • neuro-symbolic AI
  • ontology embedding

ASJC Scopus subject areas

  • General Computer Science

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