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LinkDB: a probabilistic linkage database system

Published: 12 June 2011 Publication History

Abstract

Entity linkage deals with the problem of identifying whether two pieces of information represent the same real world object. The traditional methodology computes the similarity among the entities, and then merges those with similarity above some specific threshold. We demonstrate LinkDB, an original entity storage and querying system that deals with the entity linkage problem in a novel way. LinkDB is a probabilistic linkage database that uses existing linkage techniques to generate linkages among entities, but instead of performing the merges based on these linkages, it stores them alongside the data and performs only the required merges at run-time, by effectively taking into consideration the query specifications. We explain the technical challenges behind this kind of query answering, and we show how this new mechanism is able to provide answers that traditional entity linkage mechanisms cannot.

References

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P. Agrawal, O. Benjelloun, A. D. Sarma, C. Hayworth, S. U. Nabar, T. Sugihara, and J. Widom. Trio: A system for data, uncertainty, and lineage. In VLDB, pages 1151--1154, 2006.
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P. Andritsos, A. Fuxman, and R. J. Miller. Clean answers over dirty databases: A probabilistic approach. In ICDE, 2006.
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A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate Record Detection: A Survey. TKDE, 19(1):1--16, 2007.
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A. Y. Halevy, M. J. Franklin, and D. Maier. Principles of dataspace systems. In PODS, pages 1--9, 2006.
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E. Ioannou, W. Nejdl, C. Niederée, and Y. Velegrakis. On-the-fly entity-aware query processing in the presence of linkage. PVLDB, 3(1):429--438, 2010.
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E. Ioannou, C. Niederée, and W. Nejdl. Probabilistic entity linkage for heterogeneous information spaces. In CAiSE, pages 556--570, 2008.

Cited By

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  • (2022)A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting AttributesBig Data and Cognitive Computing10.3390/bdcc60401146:4(114)Online publication date: 13-Oct-2022
  • (2012)dbTrentoACM SIGMOD Record10.1145/2380776.238078441:3(28-33)Online publication date: 5-Oct-2012
  • (2012)Embracing Uncertainty in Entity LinkingSemantic Search over the Web10.1007/978-3-642-25008-8_9(225-253)Online publication date: 28-Jan-2012

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    cover image ACM Conferences
    SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
    June 2011
    1364 pages
    ISBN:9781450306614
    DOI:10.1145/1989323

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 June 2011

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    Author Tags

    1. data integration
    2. entity linkage
    3. entity resolution

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    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    View all
    • (2022)A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting AttributesBig Data and Cognitive Computing10.3390/bdcc60401146:4(114)Online publication date: 13-Oct-2022
    • (2012)dbTrentoACM SIGMOD Record10.1145/2380776.238078441:3(28-33)Online publication date: 5-Oct-2012
    • (2012)Embracing Uncertainty in Entity LinkingSemantic Search over the Web10.1007/978-3-642-25008-8_9(225-253)Online publication date: 28-Jan-2012

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