To resolve the drawback in the existing methods for query refinement by using semantic technologies. Design/methodology/approach – The article introduces a conceptual space (ontology) in which a query refinement problem will be treated. In that space a query is modeled as a set of logic formulas, query refinement process is modeled as an inferencing process. The approach is focused on the positive conjunctive queries.
This (re)opens a question about a need for a more semantic‐based indexing of documents in the traditional information retrieval in order to improve the overall retrieval performances. This experimental study has shown that the semantics arisen (emerged) from row documents has enough quality to enable building query refinement methods that outperform the most advanced syntactic‐driven methods. Consequently, one can imagine a more emergent‐semantic based indexing process that will retain the autonomy of web data, but will significantly increase the precision of the retrieval process performed by a traditional search engine.
A novel approach for the query refinement is presented. It deals with the conceptual level of a user's query. Another benefits is that this conceptual structure emerges from the textual data automatically. The case study shows how a traditional full‐text search engine can benefit from applying this approach.
