Time-aware link prediction in RDF graphs


Abstract:

When a link is not explicitly present in an RDF dataset, it does not mean that the link could not exist in reality. Link prediction methods try to overcome this problem by finding new links in the dataset with support of a background knowledge about the already existing links in the dataset. In dynamic environments that change often and evolve over time, link prediction methods should also take into account the temporal aspects of data. In this paper, we present a novel time-aware link prediction method. We model RDF data as a tensor and take into account the time when RDF data was created. We use an ageing function to model a retention of the information over the time; lower the significance of the older information and promote more recent. Our evaluation shows that the proposed method improves quality of predictions when compared with methods that do not consider the time information.

Año de publicación:

2015

Keywords:

  • semantics
  • Tensor factorization
  • Link pbkp_rediction
  • Temporal information
  • Web APIs

Fuente:

scopusscopus

Tipo de documento:

Conference Object

Estado:

Acceso abierto

Áreas de conocimiento:

  • Minería de datos
  • Ciencias de la computación

Áreas temáticas de Dewey:

  • Funcionamiento de bibliotecas y archivos
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 9: Industria, innovación e infraestructura
  • ODS 17: Alianzas para lograr los objetivos
  • ODS 8: Trabajo decente y crecimiento económico
Procesado con IAProcesado con IA