Non-parametric Class Completeness Estimators for Collaborative Knowledge Graphs—The Case of Wikidata

Michael Luggen, Djellel Difallah, Cristina Sarasua, Gianluca Demartini, Philippe Cudré-Mauroux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it is crucial to identify underrepresented classes in the Knowledge Graph. In this paper, we tackle this problem by developing statistical techniques for class cardinality estimation in collaborative Knowledge Graph platforms. Our method is able to estimate the completeness of a class—as defined by a schema or ontology—hence can be used to answer questions such as “Does the knowledge base have a complete list of all {Beer Brands|Volcanos|Video Game Consoles}?” As a use-case, we focus on Wikidata, which poses unique challenges in terms of the size of its ontology, the number of users actively populating its graph, and its extremely dynamic nature. Our techniques are derived from species estimation and data-management methodologies, and are applied to the case of graphs and collaborative editing. In our empirical evaluation, we observe that (i) the number and frequency of unique class instances drastically influence the performance of an estimator, (ii) bursts of inserts cause some estimators to overestimate the true size of the class if they are not properly handled, and (iii) one can effectively measure the convergence of a class towards its true size by considering the stability of an estimator against the number of available instances.

Original languageEnglish (US)
Title of host publicationThe Semantic Web – ISWC 2019 - 18th International Semantic Web Conference, Proceedings
EditorsChiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon
PublisherSpringer
Pages453-469
Number of pages17
ISBN (Print)9783030307929
DOIs
StatePublished - 2019
Event18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand
Duration: Oct 26 2019Oct 30 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Semantic Web Conference, ISWC 2019
CountryNew Zealand
CityAuckland
Period10/26/1910/30/19

Keywords

  • Class cardinality
  • Class completeness
  • Edit history
  • Estimators
  • Knowledge Graph

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Luggen, M., Difallah, D., Sarasua, C., Demartini, G., & Cudré-Mauroux, P. (2019). Non-parametric Class Completeness Estimators for Collaborative Knowledge Graphs—The Case of Wikidata. In C. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. Cruz, A. Hogan, J. Song, M. Lefrançois, & F. Gandon (Eds.), The Semantic Web – ISWC 2019 - 18th International Semantic Web Conference, Proceedings (pp. 453-469). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11778 LNCS). Springer. https://doi.org/10.1007/978-3-030-30793-6_26