Using knowledge graphs to explain entity co-occurrence in Twitter

Yiwei Wang, Mark James Carman, Yuan Fang Li

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    1 Citation (Scopus)

    Abstract

    Modern Knowledge Graphs such as DBPedia contain significant information regarding Named Entities and the logical relationships which exist between them. Twitter on the other hand, contains important information on the popularity and frequency with which these entities are mentioned and discussed in combination with one another. In this paper we investigate whether these two sources of information can be used to complement and explain one another. In particular, we would like to know whether the logical relationships (a.k.a. semantic paths) which exist between pairs of known entities can help to explain the frequency with which those entities co-occur with one another in Twitter. To do this we train attranking function over semantic paths between pairs of entities. The aim of the ranker is to identify the path that most likely explains why a particular pair of entities have appeared together in a particular tweet. We train the ranking model using a number of lexical, graph-embedding and popularity-based features over semantic paths containing a single intermediate entity and demonstrate the efficacy of the model for determining why pairs of entities occur together in tweets.

    Original languageEnglish
    Title of host publicationCIKM'17
    Subtitle of host publicationProceedings of the 2017 ACM Conference on Information and Knowledge Management
    EditorsMark Sanderson, Ada Fu, Jimeng Sun, Shane Culpepper, Eric Lo, Joyce Ho, Debora Donato, Rakesh Agrawal, Yu Zheng, Carlos Castillo, Aixin Sun, Vincent S. Tseng, Chenliang Li
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages2351-2354
    Number of pages4
    ISBN (Print)9781450349185
    DOIs
    Publication statusPublished - 6 Nov 2017
    EventACM International Conference on Information and Knowledge Management 2017 - Singapore, Singapore
    Duration: 6 Nov 201710 Nov 2017
    Conference number: 26th
    http://www.cikmconference.org/CIKM2017/
    https://dl.acm.org/doi/proceedings/10.1145/3132847

    Conference

    ConferenceACM International Conference on Information and Knowledge Management 2017
    Abbreviated titleCIKM 2017
    CountrySingapore
    CitySingapore
    Period6/11/1710/11/17
    Internet address

    Keywords

    • DBPedia
    • Importance ranking
    • Information retrieval
    • Knowledge graphs
    • Machine learning
    • Microblog
    • Twitter

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