Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper we propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and Wikipedia scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, we present a set of algorithms based on our model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, Natural Language Processing, and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP.
['Gianluca Demartini', 'Claudiu S. Firan', 'Tereza Iofciu', 'Ralf Krestel', 'Wolfgang Nejdl']
A Model for Ranking Entities and Its Application to Wikipedia