Metaheuristics for Ontology-Based Information Extraction Rule Learning

Authors

  • Michel Capelle Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands
  • Flavius Frasincar Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands https://orcid.org/0000-0002-8031-758X
  • Finn van der Knaap Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

DOI:

https://doi.org/10.13052/jwe1540-9589.2549

Keywords:

metaheuristics, information extraction rules, ontology learning, Semantic Web

Abstract

The Semantic Web aims to make information intelligible for computers. In the Semantic Web, unstructured information from text is represented using ontologies, such that computers can understand text better. However, adding text information to existing ontologies by hand is time-consuming. Information extraction rules can help to automate this process. In the process of learning information extraction rules, patterns are constructed that consist of lexico-syntactic and lexico-semantic features from text, which aim to extract Resource Description Framework subject-predicate-object expressions. In this paper, we investigate the following four metaheuristics for learning ontology-based information extraction rules: Particle Swarm Optimization, 2-Phase Optimization, Ant Colony Optimization, and Genetic Algorithm (GA). We evaluate all methods using financial news data. GA gives the best F1-measure results, but the other metaheuristics are faster.

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Author Biographies

Michel Capelle, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Michel Capelle received the B.S. degree in economics and informatics from Erasmus University Rotterdam, Rotterdam, the Netherlands, in 2012, and the M.S. degree in computational economics, specialized in Semantic Web technology, at Erasmus University Rotterdam, Rotterdam, the Netherlands, in 2015. His research interests include machine learning, linked data, information extraction rule learning, natural language processing, and sentiment analysis. He has published in various conferences and journals in the area of machine learning and the Semantic Web.

Flavius Frasincar, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Flavius Frasincar received the M.S. degree in computer science, in 1996, and the M.Phil. degree in computer science, in 1997, from Politehnica University of Bucharest, Bucharest, Romania, and the P.D.Eng. degree in computer science, in 2000, and the Ph.D. degree in computer science, in 2005, from Eindhoven University of Technology, Eindhoven, the Netherlands.

Since 2005, he has been an Assistant Professor in computer science at Erasmus University Rotterdam, Rotterdam, the Netherlands. He has published in numerous conferences and journals in the areas of databases, Web information systems, personalization, machine learning, and the Semantic Web. He is a member of the editorial board of Computational Linguistics in the Netherlands Journal and co-editor-in-chief of the Journal of Web Engineering. He is also a member of the Association for Computing Machinery.

Finn van der Knaap, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, the Netherlands

Finn van der Knaap received the B.S. degree cum laude in Econometrics and Operations Research from Erasmus University Rotterdam, the Netherlands, in 2023, and the M.S. degree summa cum laude in Econometrics and Management Science, specialized in Quantitative Finance, at Erasmus University Rotterdam, the Netherlands, in 2024. In 2025, he received the M.S. degree with distinction in Artificial Intelligence at the University of Edinburgh, Scotland. His research interests include machine learning, finance, and sentiment analysis. He has published in various conferences and journals in the areas of quantitative finance, machine learning, and the Semantic Web.

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Published

2026-05-24

How to Cite

Capelle, M. ., Frasincar, F. ., & Knaap, F. van der . (2026). Metaheuristics for Ontology-Based Information Extraction Rule Learning. Journal of Web Engineering, 25(04), 699–736. https://doi.org/10.13052/jwe1540-9589.2549

Issue

2026: Vol 25 Iss 4

Section

Articles