Utilizing Knowledge Graphs for Text-Centric Information Retrieval

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The depth and breadth of content in these KGs made them not only rich sources of structured knowledge by themselves, but also valuable resources for search systems. A surge of recent developments in entity linking and entity retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications. This tutorial is the first to summarize and disseminate the progress in this emerging area to industry practitioners and researchers.

  • [DOI] L. Dietz, A. Kotov, and E. Meij, “Utilizing knowledge graphs for text-centric information retrieval,” in The 41st international acm sigir conference on research & development in information retrieval, New York, NY, USA, 2018, p. 1387–1390.
    Acmid = {3210187},
    Address = {New York, NY, USA},
    Author = {Dietz, Laura and Kotov, Alexander and Meij, Edgar},
    Booktitle = {The 41st International ACM SIGIR Conference on Research \& Development in Information Retrieval},
    Date-Added = {2018-07-26 18:24:31 +0000},
    Date-Modified = {2018-07-26 18:31:50 +0000},
    Doi = {10.1145/3209978.3210187},
    Isbn = {978-1-4503-5657-2},
    Keywords = {entity linking, entity retrieval, information retrieval, knowledge graphs},
    Location = {Ann Arbor, MI, USA},
    Numpages = {4},
    Pages = {1387--1390},
    Publisher = {ACM},
    Series = {SIGIR '18},
    Title = {Utilizing Knowledge Graphs for Text-Centric Information Retrieval},
    Url = {http://doi.acm.org/10.1145/3209978.3210187},
    Year = {2018},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/3209978.3210187},
    Bdsk-Url-2 = {https://doi.org/10.1145/3209978.3210187}}

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