Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Repairing Hidden Links in Linked Data: Enhancing the quality of RDF knowledge graphs
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Knowledge Graphs (KG) are becoming core components of most artificial intelligence applications. Linked Data, as a method of publishing KGs, allows applications to traverse within, and even out of, the graph thanks to global dereferenceable identifiers denoting entities, in the form of IRIs. However, as we show in this work, after analyzing several popular datasets (namely DBpedia, LOD Cache, and Web Data Commons JSON-LD data) many entities are being represented using literal strings where IRIs should be used, diminishing the advantages of using Linked Data. To remedy this, we propose an approach for identifying such strings and replacing them with their corresponding entity IRIs. The proposed approach is based on identifying relations between entities based on both ontological axioms as well as data profiling information and converting strings to entity IRIs based on the types of entities linked by each relation. Our approach showed 98% recall and 76% precision in identifying such strings and 97% precision in converting them to their corresponding IRI in the considered KG. Further, we analyzed how the connectivity of the KG is increased when new relevant links are added to the entities as a result of our method. Our experiments on a subset of the Spanish DBpedia data show that it could add 25% more links to the KG and improve the overall connectivity by 17%.
Nombre congreso
K-CAP 2017: Knowledge Capture Conference, 2017
Tipo de participación
Lugar del congreso
Austin, Texas
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Título de las actas
Proceedings Knowledge Capture Conference, 2017
Esta actividad pertenece a memorias de investigación
  • Autor: Mariano Rico Almodovar (UPM)
  • Autor: Nandana Sampath Mihindukulasooriya (UPM)
  • Autor: Asuncion de Maria Gomez Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group
  • Departamento: Inteligencia Artificial
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