Our paper “Dynamic Collective Entity Representations for Entity Ranking,” with Manos Tsagkias, Wouter Weerkamp, Edgar Meij and Maarten de Rijke was accepted at The 9th ACM International Conference on Web Search and Data Mining (WSDM2016). Read the extended one-page abstract (submitted to DIR 2015) here (PDF, 200kb).
Abstract: 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 is associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time, and learn the best representation at set time intervals as time evolves so as to capture the dynamics in 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.
I will post a pre-print here soon.
Update: Cool! Our paper has been selected for presentation as a long talk at the conference.
Update 2: The extended abstract of this paper has been accepted for poster + oral presentation at the 14th Dutch-Belgian Information Retrieval Workshop (DIR 2015). I’ve uploaded the slides of my DIR talk here.