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Since 2001, the semantic web community has been working hard towards creating standards which will increase the accessibility of available information on the web. Yahoo research recently reported that 30% of all HTML pages contain structured data such as microdata, RDFa, or microformat. Although multilinguality of the web is a hurdle in information access, the rapid growth of the semantic web enables us to retrieve fine grained information across the language barrier. In this thesis, firstly, we focus on developing a methodology to perform cross-lingual semantic search over structured data (knowledge base), by transforming natural language queries into SPARQL. Secondly, we focus on improving the semantic similarity and relatedness measures, to overcome the semantic gap between the vocabulary in the knowledge base and the terms appearing in the query. The preliminary results are evaluated against the QALD-2 test dataset, which achieved a F1 score of 0.46, an average precision of 0.44, and an average recall of 0.48.

Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures