Abstract
User engagement is a fundamental goal of commercial search engines. In order to increase it, they provide the users an opportunity to explore the entities related to the queries. As most of the queries can be linked to entities in knowledge bases, search engines recommend the enti- ties that are related to the users' search query. In this paper, we present Wikipedia-based Features for Entity Recommendation (WiFER) that combines different features extracted from Wikipedia in order to pro- vide related entity recommendations. We evaluate WiFER on a dataset of 4.5K search queries where each query has around 10 related entities tagged by human experts on 5-level label scale.
| Original language | English |
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| Journal | CEUR Workshop Proceedings |
| Volume | 1486 |
| Publication status | Published - 2015 |
| Event | ISWC 2015 Posters and Demonstrations Track, ISWC-P and D 2015 - co-located with the 14th International Semantic Web Conference, ISWC 2015 - Bethlehem, United States Duration: 11 Oct 2015 → … |