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ML-Based Knowledge Graph Curation: Current Solutions and Challenges

Published: 13 May 2019 Publication History

Abstract

With the success of machine learning (ML) techniques, ML has already proved a tremendous potential to impact the foundations, algorithms, and models of several data management tasks, such as error detection, data quality assessment, data cleaning, and data integration. In Knowledge Graphs, part of the data preparation and cleaning processes, such as data linking, identity disambiguation, or missing value inference and completion could be automated by making a ML model “learn” and predict the matches routinely with different degrees of supervision. This talk will survey the recent trends of applying machine learning solutions to improve and facilitate Knowledge Graph curation and enrichment, as one of the most critical tasks impacting Web search and query-answering. Finally, the talk will discuss the next research challenges in the convergence of machine learning and management of Knowledge Graph evolution and preservation.

References

[1]
Berti-Equille L., Scannapieco M. (2016). Quality of Web Data (Chapter). In the 2nd Edition of the book Data Quality: Concepts, Methodologies and Techniques, Springer, 2016
[2]
Zaveri A., Maurino A., Berti-Equille L. (2014). Web Data Quality: Current State and New Challenges, Int. J. Semant. Web Inf. Syst.,10(2):1552-6283, IGI Global.
[3]
Paulheim H. (2017) Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 8 3 489-508.
[4]
Paritosh P. (2018). The Missing Science of Knowledge Curation (Improving incentives for large-scale knowledge curation). In Companion of The Web Conference 2018, Lyon, France.

Cited By

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  • (2023)Construction of recipe knowledge graph based on user knowledge demandsJournal of Information Science10.1177/01655515221151139(016555152211511)Online publication date: 2-Feb-2023

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        cover image ACM Other conferences
        WWW '19: Companion Proceedings of The 2019 World Wide Web Conference
        May 2019
        1331 pages
        ISBN:9781450366755
        DOI:10.1145/3308560
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        • IW3C2: International World Wide Web Conference Committee

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 13 May 2019

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        Author Tags

        1. Knowledge base curation
        2. entity disambiguation
        3. knowledge graph completion

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        WWW '19
        WWW '19: The Web Conference
        May 13 - 17, 2019
        San Francisco, USA

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        View all
        • (2023)Construction of recipe knowledge graph based on user knowledge demandsJournal of Information Science10.1177/01655515221151139(016555152211511)Online publication date: 2-Feb-2023

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