A Multi-Objective Evolutionary Approach to Class Disjointness Axiom Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Nguyen:2020:WI-IAT,

• author = "Thu Huong Nguyen and Andrea G. B. Tettamanzi",
• title = "A Multi-Objective Evolutionary Approach to Class Disjointness Axiom Discovery",
• booktitle = "2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)",
• year = "2020",
• pages = "275--282",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/WIIAT50758.2020.00040",
• abstract = "The huge wealth of linked data available on the Web (also known as the Web of data), organized according to the standards of the Semantic Web, can be exploited to automatically discover new knowledge, expressed in the form of axioms, one of the essential components of ontologies. In order to overcome the limitations of existing methods for axiom discovery, we propose a two-objective grammar-based genetic programming approach that casts axiom discovery as a genetic programming problem involving the two independent criteria of axiom credibility and generality. We demonstrate the power of the proposed approach by applying it to the task of discovering class disjointness axioms involving complex class expression, a type of axioms that plays an important role in improving the quality of ontologies. We carry out experiments to determine the most appropriate parameter settings and we perform an empirical comparison of the proposed method with state-of-the-art methods proposed in the literature.",
• notes = "Also known as \cite{9457766}",

}

Genetic Programming entries for Thu Huong Nguyen Andrea G B Tettamanzi

Citations