research-article

PIDGIN: ontology alignment using web text as interlingua

Authors:

Partha Pratim Talukdar,

Tom MitchellAuthors Info & Claims

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

Pages 589 - 598

https://doi.org/10.1145/2505515.2505559

Published: 27 October 2013 Publication History

Abstract

The problem of aligning ontologies and database schemas across different knowledge bases and databases is fundamental to knowledge management problems, including the problem of integrating the disparate knowledge sources that form the semantic web's Linked Data [5].

We present a novel approach to this ontology alignment problem that employs a very large natural language text corpus as an interlingua to relate different knowledge bases (KBs). The result is a scalable and robust method (PIDGIN) that aligns relations and categories across different KBs by analyzing both (1) shared relation instances across these KBs, and (2) the verb phrases in the text instantiations of these relation instances. Experiments with PIDGIN demonstrate its superior performance when aligning ontologies across large existing KBs including NELL, Yago and Freebase. Furthermore, we show that in addition to aligning ontologies, PIDGIN can automatically learn from text, the verb phrases to identify relations, and can also type the arguments of relations of different KBs.

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https://dl.acm.org/doi/10.1016/j.inffus.2022.09.012
Cheng BZhu JGuo M(2022)MultiJAFNeurocomputing10.1016/j.neucom.2022.05.058500:C(581-591)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.05.058
Wang LChan RZeng T(2021)Probabilistic Semi-Supervised Learning via Sparse Graph Structure LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297960732:2(853-867)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979607
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Index Terms

PIDGIN: ontology alignment using web text as interlingua
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks

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cover image ACM Conferences

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

Copyright © 2013 ACM.

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Published: 27 October 2013

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

October 27 - November 1, 2013

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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

Zhu JHuang CDe Meo P(2023)DFMKEInformation Fusion10.1016/j.inffus.2022.09.01290:C(111-119)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.inffus.2022.09.012
Cheng BZhu JGuo M(2022)MultiJAFNeurocomputing10.1016/j.neucom.2022.05.058500:C(581-591)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.05.058
Wang LChan RZeng T(2021)Probabilistic Semi-Supervised Learning via Sparse Graph Structure LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297960732:2(853-867)Online publication date: Feb-2021
https://doi.org/10.1109/TNNLS.2020.2979607
Li PLu HZheng QLi SPan G(2020)HisRect: Features from Historical Visits and Recent Tweet for Co-Location JudgementIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2934686(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2019.2934686
Fukumoto FSuzuki YWangpoonsarp AJi M(2019)Integrating Local and Global Data View for Bilingual Sense CorrespondencesKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-15640-4_12(224-241)Online publication date: 15-Mar-2019
https://doi.org/10.1007/978-3-030-15640-4_12
Zhuang CMa QChampin PGandon FMédini LLalmas MIpeirotis P(2018)Dual Graph Convolutional Networks for Graph-Based Semi-Supervised ClassificationProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186116(499-508)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186116
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
Koutraki MPreda NVodislav D(2017)Online Relation Alignment for Linked DatasetsThe Semantic Web10.1007/978-3-319-58068-5_10(152-168)Online publication date: 28-May-2017
https://dl.acm.org/doi/10.1007/978-3-319-58068-5_10
Rodríguez MGoldberg SWang D(2016)SigmaKBProceedings of the VLDB Endowment10.14778/3007263.30073139:13(1577-1580)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.14778/3007263.3007313
Chen YGoldberg SWang DJohri SÖzcan FKoutrika GMadden S(2016)Ontological PathfindingProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2882954(835-846)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1145/2882903.2882954
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