VICKEY: Mining Conditional Keys on Knowledge Bases

Danai Symeonidou (1) , Luis Galárraga (2) , Nathalie Pernelle (3, 4) , Fatiha Saïs (3, 4) , Fabian Suchanek (5)

1 INRA - Institut National de la Recherche Agronomique
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 LRI - Laboratoire de Recherche en Informatique
4 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes (LRI)
5 INFRES - Département Informatique et Réseaux

A conditional key is a key constraint that is valid in only a part of the data. In this paper, we show how such keys can be mined automatically on large knowledge bases (KBs). For this, we combine techniques from key mining with techniques from rule mining. We show that our method can scale to KBs of millions of facts. We also show that the conditional keys we mine can improve the quality of entity linking by up to 47 percentage points.

Domains: Computer Science [cs]; Artificial Intelligence [cs.AI]

Danai Symeonidou, Luis Galárraga, Nathalie Pernelle, Fatiha Saïs, Fabian Suchanek. VICKEY: Mining Conditional Keys on Knowledge Bases. International Semantic Web Conference (ISWC), Oct 2017, Vienne, Austria. pp.661-677. ⟨hal-01647597⟩