Ontology population and enrichment: state of the art

Authors:
Georgios Petasis;Vangelis Karkaletsis;Georgios Paliouras;Anastasia Krithara;Elias Zavitsanos
Affiliations:
Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Paraskevi, Attiki, Greece;Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Paraskevi, Attiki, Greece;Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Paraskevi, Attiki, Greece;Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Paraskevi, Attiki, Greece;Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Paraskevi, Attiki, Greece
Venue:
Knowledge-driven multimedia information extraction and ontology evolution
Year:
2011

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Abstract

Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project1, due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches.