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PATTY: a taxonomy of relational patterns with semantic types

Authors:

Ndapandula Nakashole,

Gerhard Weikum,

Fabian SuchanekAuthors Info & Claims

EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

Pages 1135 - 1145

Published: 12 July 2012 Publication History

Abstract

This paper presents PATTY: a large resource for textual patterns that denote binary relations between entities. The patterns are semantically typed and organized into a subsumption taxonomy. The PATTY system is based on efficient algorithms for frequent itemset mining and can process Web-scale corpora. It harnesses the rich type system and entity population of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. Random-sampling-based evaluation shows a pattern accuracy of 84.7%. PATTY has 8,162 subsumptions, with a random-sampling-based precision of 75%. The PATTY resource is freely available for interactive access and download.

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cover image DL Hosted proceedings

EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

July 2012

1573 pages

General Chair:
Jun'ichi Tsujii
Microsoft Research Asia
,
Program Chairs:
James Henderson
Xerox Research Centre Europe
,
Marius Pasca
Google

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 12 July 2012

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Overall Acceptance Rate 73 of 234 submissions, 31%

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Hong YLi ZQu JLiang JLuo YZhang MXiao YWang WAl Hasan MXiong L(2022)Can We Have Both Fish and Bear's Paw?Proceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557251(758-767)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557251
Qi SZheng LShang F(2021)Dependency Parsing-based Entity Relation Extraction over Chinese Complex TextACM Transactions on Asian and Low-Resource Language Information Processing10.1145/345027320:4(1-34)Online publication date: 9-Jun-2021
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Zeng QLin JYu WCleland-Huang JJiang MZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Enhancing Taxonomy Completion with Concept Generation via Fusing Relational RepresentationsProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467308(2104-2113)Online publication date: 14-Aug-2021
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Huang JXie YMeng YZhang YHan JGupta RLiu YShah MRajan STang JPrakash B(2020)CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation TransferringProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403244(1928-1936)Online publication date: 23-Aug-2020
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