Natural language interaction with the web of data by mining its textual side

The Semantic Web is an extension of the classical web. The data and schemas it adds coexist with the documents that were already linked and available. This not only allows interoperability, reusability and potentially unforeseen applications of opened data, but it also creates a unique situation of availability on the web of huge collections of the same pieces of information available at the same time as text and as structured data. An interesting example is the couple Wikipedia-DBpedia: exploiting these interlinked structured and unstructured data sources in parallel can offer a great potential for both Natural Language Processing and Semantic Web applications. Starting from these observations, this paper addresses the problem of enhancing interactions between non-expert users and data available on the Web. In particular, we present QAKiS, a system for open domain Question Answering over linked data, that addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. In the current version, the relational patterns are automatically extracted from Wikipedia, while DBpedia is the data set to be queried using a natural language interface.