Abstract. This paper describes the participation of the INRIA group in the INEX 2007 XML entity ranking and ad hoc tracks. We developed a system for ranking Wikipedia entities in answer to a query. Our approach utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the examples (when provided) to improve the effectiveness of entity ranking. Our experiments on both the training and the testing data sets demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity retrieval effectiveness. We also use our system for the ad hoc tasks by inferring target categories from the title of the query. The results were worse than when using a full-text search engine, which confirms our hypothesis that ad hoc retrieval and entity retrieval are two different tasks. 1

Abstract. Many realistic user tasks involve the retrieval of specific entities instead of just any type of documents. Examples of information needs include ‘Countries where one can pay with the euro ’ or ‘Impressionist art museums in The Netherlands’. The Initiative for Evaluation of XML Retrieval (INEX) started the XML Entity Ranking track (INEX-XER) to create a test collection for entity retrieval in Wikipedia. Entities are assumed to correspond to Wikipedia entries. The goal of the track is to evaluate how well systems can rank entities in response to a query; the set of entities to be ranked is assumed to be loosely defined either by a generic category (entity ranking) or by some example entities (list completion). This track overview introduces the track setup, and discusses the implications of the new relevance notion for entity ranking in comparison to ad hoc retrieval. 1

This paper describes experiments carried out at NiCT for the TREC 2009 Entity Ranking track. Our main study is to develop an effective approach to rank entities via measuring the “similarities ” between supporting snippets of entities and input query. Three models are implemented to this end. 1) The DLM regards entity ranking as a task of calculating the probabilities of generating input query given supporting snippets of entities via language model. 2) The RSVM ranks entities via a supervised Ranking SVM. 3) The CSVM, an unsupervised model, ranks entities according to the probabilities of input query belonging to topics represented by entities and their supporting snippets via SVM classifier. The evaluation shows that the DLM is the best on P@10, while the RSVM outperforms the others on nDCG. 1

Abstract. Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking. 1

Entity search is an emerging research topic in Information Retrieval, where the goal is to rank not documents, but entities in response to a given query. A particularly challenging example of this search scenario is when a user’s underlying information need is for a list of entities related to a given entity, represented in the query. In this paper, we propose to tackle this problem as a voting process, by considering the occurrence of an entity among the top ranked documents for a given query as a vote for the existence of a relationship between this and the entity in the query. Our proposed approach is evaluated using a large Web test collection, in the context of the TREC 2009 Entity track. The results attest the effectiveness of our approach when compared to the top participants at TREC, with unparalleled gains in terms of recall. Moreover, through a comprehensive failure analysis, we uncover important issues to be considered when tackling this new search scenario and draw valuable insights towards achieving an effective related entity search performance.

Abstract. This paper describes the participation of the INRIA group in the INEX 2007 XML entity ranking and ad hoc tracks. We developed a system for ranking Wikipedia entities in answer to a query. Our approach utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the examples (when provided) to improve the effectiveness of entity ranking. Our experiments on the training data set demonstrate that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve entity retrieval effectiveness. We also use our system for the ad hoc tasks by inferring target categories from the title of the query. The results were worse than when using a full-text search engine, which confirms our hypothesis that ad hoc retrieval and entity retrieval are two different tasks. 1

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