Learning Class Disjointness Axioms Using Grammatical Evolution

International audienceoday, with the development of the Semantic Web, LinkedOpen Data (LOD), expressed using the Resource Description Frame-work (RDF), has reached the status of “big data” and can be consideredas a giant data resource from which knowledge can be discovered. Theprocess of learning knowledge defined in terms of OWL 2 axioms fromthe RDF datasets can be viewed as a special case of knowledge discov-ery from data or “data mining”, which can be called “RDF mining”.The approaches to automated generation of the axioms from recordedRDF facts on the Web may be regarded as a case of inductive reasoningand ontology learning. The instances, represented by RDF triples, playthe role of specific observations, from which axioms can be extracted bygeneralization. Based on the insight that discovering new knowledge isessentially an evolutionary process, whereby hypotheses are generatedby some heuristic mechanism and then tested against the available evi-dence, so that only the best hypotheses survive, we propose the use ofGrammatical Evolution, one type of evolutionary algorithm, for miningdisjointness OWL 2 axioms from an RDF data repository such as DBpe-dia. For the evaluation of candidate axioms against the DBpedia dataset,we adopt an approach based on possibility theory

A Multi-Objective Evolutionary Approach to Class Disjointness Axiom Discovery

International audienceThe 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

An Evolutionary Approach to Class Disjointness Axiom Discovery

International audienceAxiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class dis-jointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage