TY - GEN
T1 - Non-parametric Class Completeness Estimators for Collaborative Knowledge Graphs—The Case of Wikidata
AU - Luggen, Michael
AU - Difallah, Djellel
AU - Sarasua, Cristina
AU - Demartini, Gianluca
AU - Cudré-Mauroux, Philippe
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Class cardinality
KW - Class completeness
KW - Edit history
KW - Estimators
KW - Knowledge Graph
UR - http://www.scopus.com/inward/record.url?scp=85075714802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075714802&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30793-6_26
DO - 10.1007/978-3-030-30793-6_26
M3 - Conference contribution
AN - SCOPUS:85075714802
SN - 9783030307929
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 453
EP - 469
BT - The Semantic Web – ISWC 2019 - 18th International Semantic Web Conference, Proceedings
A2 - Ghidini, Chiara
A2 - Hartig, Olaf
A2 - Maleshkova, Maria
A2 - Svátek, Vojtech
A2 - Cruz, Isabel
A2 - Hogan, Aidan
A2 - Song, Jie
A2 - Lefrançois, Maxime
A2 - Gandon, Fabien
PB - Springer
T2 - 18th International Semantic Web Conference, ISWC 2019
Y2 - 26 October 2019 through 30 October 2019
ER -