Discovering and merging keyword senses using ontology matching

During the last years we are witnessing how the use of keywords has become the standard input when searching the Web. As opposite to the syntactic searches performed by traditional web search engines, the current research challenge is a semantics-guided information retrieval. The increasing pools of ontologies available on the Web can help to discover the semantics of user keywords and this information is priceless for many tasks, including new semantic search engines. In this paper we propose a system that takes as input a list of keywords provided by the user and discovers their possible meanings by consulting the knowledge represented by many (heterogeneous and distributed) ontologies. These keyword senses are semantically enriched with the synonym terms found during the ontology matching process: A synonymy measure based on statistics techniques and ontological similarity is used to integrate senses that are similar enough.