Study On Semantic Search User Interface

Semantic search is one important research topic in the Semantic Web community. By making full use of the semantics embedded in the underlying data, semantic search promises to provide the users with precise answers, rather than documents which only contain the typed keywords. However, such semantic search requires the users to input formal logic queries, as well as some knowledge about the schema and vocabulary of the underlying knowledge base, which is beyond the desire and capability of casual users. Therefore, it is valuable to study on how to provide the casual users a friendly semantic search interface.In this paper, by making use of the current Semantic Web technologies and studying on other similar structural data, we propose a framework which accepts natural language or keyword queries for semantic search. Within this framework, by utilizing ontology knowledge and existing natural language processing techniques and tools, the user input queries are first mapped to entities in the ontology and then transformed to an intermediate data model called triples, which is finally interpreted as formal SPARQL queries that can be optimized and executed by a state-of-the-art engine. During the process of translating a natural language query into formal logic query, we analyze the major difficulties and propose a two-stage transformation approach accordingly. We also propose a BaseNP-based analyzing technique to extract syntactic information from a parse tree. While translating a keyword query, we pay most of our efforts on disambiguating the multiple results produced, and propose a ranking method for that.The experiments show that the proposed approach achieved a good performance on translation quality, which also prove that natural language and keyword interface are effective approach for semantic search. In addition, we also analyze the limitations of our approach and discuss on future work.