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Uncovering Hidden Semantics of Set Information in Knowledge Bases

15 Pages Posted: 20 Jan 2021 Publication Status: Accepted

See all articles by Shrestha Ghosh

Shrestha Ghosh

Max Planck Institute for Informatics

Simon Razniewski

Max Planck Institute for Informatics

Gerhard Weikum

Max Planck Society for the Advancement of the Sciences - Institute for Computer Science

Abstract

Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyze the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo.

Suggested Citation

Ghosh, Shrestha and Razniewski, Simon and Weikum, Gerhard, Uncovering Hidden Semantics of Set Information in Knowledge Bases. Journal of Web Semantics First Look , Available at SSRN: https://ssrn.com/abstract=3769926 or http://dx.doi.org/10.2139/ssrn.3769926

Shrestha Ghosh (Contact Author)

Max Planck Institute for Informatics ( email )

Germany

Simon Razniewski

Max Planck Institute for Informatics ( email )

Germany

Gerhard Weikum

Max Planck Society for the Advancement of the Sciences - Institute for Computer Science ( email )

Saarbruecken
Germany

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