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Discovering relations using matrix factorization methods

Authors:

Ervina Cergani,

Pauli MiettinenAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1549 - 1552

https://doi.org/10.1145/2505515.2507841

Published: 27 October 2013 Publication History

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Abstract

Traditional relation extraction methods work on manually defined relations and typically expect manually labelled extraction patterns for each relation. This strongly limits the scalability of these systems. In Open Relation Extraction (ORE), the relations are identified automatically based on co-occurrences of "surface relations" (contexts) and entity pairs. The recently-proposed methods for ORE use partition clustering to find the relations. In this work we propose the use of matrix factorization methods instead of clustering. Specifically, we study Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorization (BMF). These methods overcome many problems inherent in clustering and perform better than the k-means clustering in our evaluation.

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Cited By

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Parfenov DGrishina LZhigalov ABolodurina I(2021)Research of Genetic Optimization Algorithms in the Design of VLAN2021 29th Telecommunications Forum (TELFOR)10.1109/TELFOR52709.2021.9653295(1-4)Online publication date: 23-Nov-2021
https://doi.org/10.1109/TELFOR52709.2021.9653295
Saenko IKotenko IAuger AStützle T(2019)A role-base approach and a genetic algorithm for VLAN design in large critical infrastructuresProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326853(1643-1650)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326853
Saenko IKotenko I(2018)Genetic Algorithms for Solving Problems of Access Control Design and Reconfiguration in Computer NetworksACM Transactions on Internet Technology10.1145/309389818:3(1-21)Online publication date: 6-Mar-2018
https://dl.acm.org/doi/10.1145/3093898
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Index Terms

Discovering relations using matrix factorization methods
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Parfenov DGrishina LZhigalov ABolodurina I(2021)Research of Genetic Optimization Algorithms in the Design of VLAN2021 29th Telecommunications Forum (TELFOR)10.1109/TELFOR52709.2021.9653295(1-4)Online publication date: 23-Nov-2021
https://doi.org/10.1109/TELFOR52709.2021.9653295
Saenko IKotenko IAuger AStützle T(2019)A role-base approach and a genetic algorithm for VLAN design in large critical infrastructuresProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326853(1643-1650)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326853
Saenko IKotenko I(2018)Genetic Algorithms for Solving Problems of Access Control Design and Reconfiguration in Computer NetworksACM Transactions on Internet Technology10.1145/309389818:3(1-21)Online publication date: 6-Mar-2018
https://dl.acm.org/doi/10.1145/3093898
Yan JWang CCheng WGao MZhou A(2018)A retrospective of knowledge graphsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5228-912:1(55-74)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11704-016-5228-9
Subhashree SIrny RSreenivasa Kumar P(2017)Review of Approaches for Linked Data Ontology EnrichmentDistributed Computing and Internet Technology10.1007/978-3-319-72344-0_2(27-49)Online publication date: 29-Nov-2017
https://doi.org/10.1007/978-3-319-72344-0_2
Cergani EProksch SNadi SMezini MBaysal OCzerwonka JGuerrouj LLo DMurphy B(2016)Addressing scalability in API method call analyticsProceedings of the 2nd International Workshop on Software Analytics10.1145/2989238.2989240(1-7)Online publication date: 13-Nov-2016
https://dl.acm.org/doi/10.1145/2989238.2989240
Saenko IKotenko I(2015)Reconfiguration of Access Schemes in Virtual Networks of the Internet of Things by Genetic AlgorithmsIntelligent Distributed Computing IX10.1007/978-3-319-25017-5_15(155-165)Online publication date: 18-Oct-2015
https://doi.org/10.1007/978-3-319-25017-5_15
Konstantinova N(2014)Review of Relation Extraction Methods: What Is New Out There?Analysis of Images, Social Networks and Texts10.1007/978-3-319-12580-0_2(15-28)Online publication date: 7-Nov-2014
https://doi.org/10.1007/978-3-319-12580-0_2

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