An Approximation Approach for Semantic Queries of Naïve Users by a New Query Language

This paper focuses on querying semi structured data such as RDF data, using a proposed query language for the non-expert user, in the context of a lack knowledge structure. This language is inspired from the semantic regular path queries. The problem appears when the user specifies concepts that are not in the structure, as approximation approaches, operations based on query modifications and concepts hierarchies only are not able to find valuable solutions. Indeed, these approaches discard concepts that may have common meaning, therefore for a better approximation; the approach must better understand the user in order to obtain relevant answers. Starting from this, an approximation approach using a new query language, based on similarity meaning obtained from WordNet is proposed. A new similarity measure is then defined and calculated from the concepts synonyms in WordNet, the measure is then used in every step of the approach for helping to find relations between graph nodes and user concepts. The new proposed similarity can be used for enhancing the previous approximate approaches. The approach starts by constructing a graph pattern ( ) from the query and finalized by outputting a set of approximate graph patterns containing the results ranked in decreasing order of the approximation value level.