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  Enriching Knowledge Bases with Counting Quantifiers

Mirza, P., Razniewski, S., Darari, F., & Weikum, G. (2018). Enriching Knowledge Bases with Counting Quantifiers. Retrieved from http://arxiv.org/abs/1807.03656.

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arXiv:1807.03656.pdf (Preprint), 387KB
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arXiv:1807.03656.pdf
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File downloaded from arXiv at 2018-08-06 08:52 The 17th International Semantic Web Conference (ISWC 2018)
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 Urheber:
Mirza, Paramita1, Autor           
Razniewski, Simon1, Autor           
Darari, Fariz2, Autor
Weikum, Gerhard1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

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Schlagwörter: Computer Science, Computation and Language, cs.CL
 Zusammenfassung: Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.

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Sprache(n): eng - English
 Datum: 2018-07-102018
 Publikationsstatus: Online veröffentlicht
 Seiten: 16 p.
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 Identifikatoren: arXiv: 1807.03656
URI: http://arxiv.org/abs/1807.03656
BibTex Citekey: Mirza_arXiv:1807.03656
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