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Constructing and Mining Web-Scale Knowledge Graphs: WWW 2015 Tutorial

Published: 18 May 2015 Publication History

Abstract

Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, Google's Knowledge Graph, YAGO, Facebook's Entity Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.

Cited By

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  • (2020)Machine Learning for the Semantic WebSemantic Web10.3233/SW-20038811:1(195-203)Online publication date: 1-Jan-2020
  • (2016)Efficient energy-based embedding models for link prediction in knowledge graphsJournal of Intelligent Information Systems10.1007/s10844-016-0414-747:1(91-109)Online publication date: 1-Aug-2016

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  1. Constructing and Mining Web-Scale Knowledge Graphs: WWW 2015 Tutorial

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    Published In

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    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    • IW3C2: International World Wide Web Conference Committee

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 May 2015

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    Author Tags

    1. data mining
    2. knowledge graphs

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    WWW '15
    Sponsor:
    • IW3C2

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2020)Machine Learning for the Semantic WebSemantic Web10.3233/SW-20038811:1(195-203)Online publication date: 1-Jan-2020
    • (2016)Efficient energy-based embedding models for link prediction in knowledge graphsJournal of Intelligent Information Systems10.1007/s10844-016-0414-747:1(91-109)Online publication date: 1-Aug-2016

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