Entity Ranking in Wikipedia: Utilising Categories, Links and Topic Difficulty Prediction

Entity Ranking in Wikipedia: Utilising Categories, Links and Topic Difficulty Prediction - scientific work related to Wikipedia quality published in 2010, written by Jovan Pehcevski, James A. Thom, Anne-Marie Vercoustre and Vladimir Naumovski.

Overview

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 names of 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, authors describe a system authors developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. Authors also extend entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, authors 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 entity ranking system. Authors experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.