Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Type Prediction of RDF Knowledge Graphs using Binary Classifiers with Structural Data
Año:2018
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
Type information, which is useful for responding many queries, plays a key role in Semantic Web. Nevertheless, it is common that type information of some instances is not present in knowledge graphs. Thus, type prediction of a given instance using background knowledge is an important knowledge graph completion task. To this end, the objective of this paper is to propose a data-driven type prediction approach using the structural information of the given instance utilising machine learning techniques. The experiments presented in the paper demonstrate that a binary classifier with structural information as features can be effectively used for type prediction of RDF knowledge graphs with high accuracy. The accuracy of the classifier is related to the diversity of training data as well as the how conceptually similar are the different classes in the training and test data. Further, the experiments demonstrate that it is possible to build universal classifiers to a given class, i.e., a model training on one dataset can produce good predictions for another dataset in cases where training data contains conceptually different classes. For example, a model training on the English DBpedia can be used to predict types of the Spanish DBpedia.
Internacional
Si
Nombre congreso
TourismKG workshop at ICWE 2018
Tipo de participación
960
Lugar del congreso
Cáceres, España
Revisores
Si
ISBN o ISSN
978-3-030-03056-8
DOI
Fecha inicio congreso
05/06/2018
Fecha fin congreso
05/06/2018
Desde la página
279
Hasta la página
287
Título de las actas
Current trends in web engineering : ICWE 2018 Lecture Notes in Computer Science, vol 11153. Springer, Cham
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Nandana Sampath Mihindukulasooriya (UPM)
  • Autor: Mariano Rico Almodovar (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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