Predicting completeness in knowledge bases

Luis Galárraga; Simon Razniewski; Antoine Amarilli; Fabian M. Suchanek

doi:10.1145/3018661.3018739

Predicting completeness in knowledge bases

Luis Galárraga, Simon Razniewski, Antoine Amarilli, Fabian M. Suchanek

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

81 Citations (Scopus)

Abstract

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where the knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact inference.

Original language	English
Title of host publication	WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
Number of pages	9
Publisher	Association for Computing Machinery
Publication date	2 Feb 2017
Pages	375-383
ISBN (Electronic)	9781450346757
DOIs	https://doi.org/10.1145/3018661.3018739
Publication status	Published - 2 Feb 2017
Event	10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom Duration: 6 Feb 2017 → 10 Feb 2017

Conference

Conference	10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/Territory	United Kingdom
City	Cambridge
Period	06/02/2017 → 10/02/2017
Sponsor	ACM SIGKDD, ACM SIGMOD, ACM SIGWEB, Special Interest Group on Information Retrieval (ACM SIGIR)

Keywords

Incompleteness
Knowledge bases
Quality
Recall

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10.1145/3018661.3018739

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title = "Predicting completeness in knowledge bases",

abstract = "Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where the knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact inference.",

keywords = "Incompleteness, Knowledge bases, Quality, Recall",

author = "Luis Gal{\'a}rraga and Simon Razniewski and Antoine Amarilli and Suchanek, {Fabian M.}",

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booktitle = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",

publisher = "Association for Computing Machinery",

address = "United States",

note = "10th ACM International Conference on Web Search and Data Mining, WSDM 2017 ; Conference date: 06-02-2017 Through 10-02-2017",

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Galárraga, L, Razniewski, S, Amarilli, A & Suchanek, FM 2017, Predicting completeness in knowledge bases. in WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, pp. 375-383, 10th ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, 06/02/2017. https://doi.org/10.1145/3018661.3018739

Predicting completeness in knowledge bases. / Galárraga, Luis; Razniewski, Simon; Amarilli, Antoine et al.
WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, 2017. p. 375-383.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Predicting completeness in knowledge bases

AU - Galárraga, Luis

AU - Razniewski, Simon

AU - Amarilli, Antoine

AU - Suchanek, Fabian M.

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Y1 - 2017/2/2

N2 - Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where the knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact inference.

AB - Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where the knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact inference.

KW - Incompleteness

KW - Knowledge bases

KW - Quality

KW - Recall

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M3 - Article in proceeding

AN - SCOPUS:85015308862

SP - 375

EP - 383

BT - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery

T2 - 10th ACM International Conference on Web Search and Data Mining, WSDM 2017

Y2 - 6 February 2017 through 10 February 2017

ER -

Predicting completeness in knowledge bases

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Keywords

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