Representativeness of Knowledge Bases with the Generalized Benford's Law

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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.

Publication

Representativeness of Knowledge Bases with the Generalized Benford's Law
Arnaud Soulet, Arnaud Giacometti, Béatrice Markhoff and Fabian M. Suchanek
Full paper at ISWC18

Running example

We provide the queries, the distributions and the analysis of this running example about the population here.

Queries and results of experiments

• Cities in France: We validate our method on the number of inhabitants of French cities from DBpedia (most-populated, least-populated and random).
• Analysis of DBpedia (France): We provide an overview of the representativeness of DBpedia (France) (counting transformation and numerical transformation).

Source code

We provide the Java source code of the prototype here.