The following WSDM 2016 paper is online now:

  • David Graus, Manos Tsagkias, Wouter Weerkamp, Edgar Meij, and Maarten de Rijke. Learning dynamic collective entity representations for entity ranking. In WSDM 2016: The 9th International Conference on Web Search and Data Mining, page 595–604. ACM, February 2016. Bibtex, PDF
    @inproceedings{graus-dynamic-2016,
    Author = {Graus, David and Tsagkias, Manos and Weerkamp, Wouter and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {WSDM 2016: The 9th International Conference on Web Search and Data Mining},
    Date-Added = {2015-10-12 18:42:35 +0000},
    Date-Modified = {2016-05-22 17:59:44 +0000},
    Month = {February},
    Pages = {595--604},
    Publisher = {ACM},
    Title = {Learning dynamic collective entity representations for entity ranking},
    Year = {2016}}

Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity’s description in a knowledge base, and the way people refer to the entity when searching for it. To counter this issue we propose a method for constructing dynamic collective entity representations. We collect entity descriptions from a variety of sources and combine them into a single entity representation by learning to weight the content from different sources that are associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time and learn the best representation as time evolves so as to capture the dynamics of how people search entities. Incorporating dynamic description sources into dynamic collective entity representations improves retrieval effectiveness by 7% over a state-of-the-art learning to rank baseline. Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations.