Multipedia: Enriching DBpedia with Multimedia Information

García-Silva, A.; Jakob, Max; Mendes, Pablo N. y Bizer, Christian (2011). Multipedia: Enriching DBpedia with Multimedia Information. En: "3rd The Sixth International Conference on Knowledge Capture, K-CAP 2011", 25/06/2011-29/06/2011, Banff, Alberta, Canada.

Descripción

Título: Multipedia: Enriching DBpedia with Multimedia Information
Autor/es:
  • García-Silva, A.
  • Jakob, Max
  • Mendes, Pablo N.
  • Bizer, Christian
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 3rd The Sixth International Conference on Knowledge Capture, K-CAP 2011
Fechas del Evento: 25/06/2011-29/06/2011
Lugar del Evento: Banff, Alberta, Canada
Título del Libro: 3rd The Sixth International Conference on Knowledge Capture, K-CAP 2011
Fecha: Junio 2011
Materias:
Palabras Clave Informales: oeg, España Virtual
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Grupo Investigación UPM: Ontology Engineering Group – OEG
Licencias Creative Commons: Ninguna

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Resumen

Enriching knowledge bases with multimedia information makes it possible to complement textual descriptions with visual and audio information. Such complementary information can help users to understand the meaning of assertions, and in general improve the user experience with the knowledge base. In this paper we address the problem of how to enrich ontology instances with candidate images retrieved from existing Web search engines. DBpedia has evolved into a major hub in the Linked Data cloud, interconnecting millions of entities organized under a consistent ontology. Our approach taps into the Wikipedia corpus to gather context information for DBpedia instances and takes advantage of image tagging information when this is available to calculate semantic relatedness between instances and candidate images. We performed experiments with focus on the particularly challenging problem of highly ambiguous names. Both methods presented in this work outperformed the baseline. Our best method leveraged context words from Wikipedia,tags from Flickr and type information from DBpedia to achieve an average precision of 80%.

Más información

ID de Registro: 6964
Identificador DC: http://oa.upm.es/6964/
Identificador OAI: oai:oa.upm.es:6964
Depositado por: Dr Oscar Corcho
Depositado el: 09 May 2011 10:25
Ultima Modificación: 20 Abr 2016 16:04
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