Semantic-Based Composite Document Ranking

The traditional information retrieval techniques mainly employ statistics of words in document text and/or the link structures of document sets to rank, which have been used successfully in the global web search. However, they produce unsatisfied results for Enterprise search (ES), because ES is very different from Web search. This paper proposes a novel rank approach fitting for the ES environment. With the support of an ontology describing prior knowledge about the target domain, we first mine semantic information (concepts and relations between them) from queries (documents) with which to understand the query intentions (document contents) and exploit them for evaluating the query-document relevance, and then the semantic linkages between documents are built and consumed for evaluating the document importance, finally, the above two evaluations are integrated to produce the final ranking list. Experiments show that our approach results in significant improvements over existing solutions.