Abstract
Question Answering Systems (QASs) are usually built behind queries described by short texts. The explosion of knowledge graphs and Linked Open Data motivates researchers for constructing QASs over these rich data resources. The shortness nature of user questions contributes to complicate the problem of Entity Linking, widely studied for long texts. In this paper, we propose an approach, called WeLink, based on the context and types of entities of a given query. The context of an entity is described by synonyms of the words used in the question and the definition of the named entity, whereas the type describes the category of the entity. During the named entity recognition step, we first identify different entities, their types, and contexts (by the means of the Wordnet). The expanded query is then executed on the target knowledge base, where several candidates are obtained with their contexts and types. Similarity distances among these different contexts and types are computed in order to select the appropriate candidate. Finally, our system is evaluated on a dataset with 5000 questions and compared with some well-known Entity Linking systems.