Analysing the Trade-Off Between Computational Performance and Representation Richness in Ontology-Based Systems

Autor:	María del Pilar Villamil, Ghassan Beydoun, Fabian C. Peña, Salvatore F. Pileggi
Rok vydání:	2019
Předmět:	Computer science Interoperability Web Ontology Language Ontology (information science) Data science Data modeling Metadata Ontology Semantic technology Artificial Intelligence & Image Processing Representation (mathematics) Semantic Web computer computer.programming_language
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030227494 ICCS (5)
Popis:	© Springer Nature Switzerland AG 2019. As the result of the intense research activity of the past decade, Semantic Web technology has achieved a notable popularity and maturity. This technology is leading the evolution of the Web via inter-operability by providing structured metadata. Because of the adoption of rich data models on a large scale to support the representation of complex relationships among concepts and automatic reasoning, the computational performance of ontology-based systems can significantly vary. In the evaluation of such a performance, a number of critical factors should be considered. Within this paper, we provide an empirical framework that yields an extensive analysis of the computational performance of ontology-based systems. The analysis can be seen as a decision tool in managing the constraints of representational requirements versus reasoning performance. Our approach adopts synthetic ontologies characterised by an increasing level of complexity up to OWL 2 DL. The benefits and the limitations of this approach are discussed in the paper.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf0842d1b726cf2739410a07ce44a83d https://doi.org/10.1007/978-3-030-22750-0_18 Zobrazit plný text záznamu