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Conference Papers Year : 2018

Representativeness of Knowledge Bases with the Generalized Benford’s Law

Abstract

Knowledge bases (KBs) such as DBpedia, Wikidata, and YAGO contain a huge number of entities and facts. Several recent works induce rules or calculate statistics on these KBs. Most of these methods are based on the assumption that the data is a representative sample of the studied universe. Unfortunately, KBs are biased because they are built from crowdsourcing and opportunistic agglomeration of available databases. This paper aims at approximating the representativeness of a relation within a knowledge base. For this, we use the generalized Benford's law, which indicates the distribution expected by the facts of a relation. We then compute the minimum number of facts that have to be added in order to make the KB representative of the real world. Experiments show that our unsupervised method applies to a large number of relations. For numerical relations where ground truths exist, the estimated representativeness proves to be a reliable indicator.
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Dates and versions

hal-01824490 , version 1 (16-05-2022)

Identifiers

  • HAL Id : hal-01824490 , version 1

Cite

Arnaud Soulet, Arnaud Giacometti, Béatrice Bouchou Markhoff, Fabian M. Suchanek. Representativeness of Knowledge Bases with the Generalized Benford’s Law. ISWC, Oct 2018, Monterey, CA, United States. ⟨hal-01824490⟩
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