Keyword-search engines (e.g. Web-search) usually can be outperformed by a specialized system optimized for a spe- cic domain, type of data, or queries (8, 2, 12, 5, 11, 9). For example, Halevy et. al. in (13) demonstrate how a special- ized Google Fusion Tables spatial search can outperform the general-purpose Google Web-search on bike trails search in San Francisco Bay Area. At the same time, Web content providers usually exhibit a specic focus for their postings. For example, information at http://www.csail.mit.edu is devoted to Computer Science research and education, Han- nah Montana is mostly tweeting about music, and the same is true for most sources. This paper describes the work in progress on a new Type- aware Web-search system that uses topical focus of informa- tion sources to process a large class of queries better than a regular Web search-engine. It leverages semantic proles similar to (10, 6, 7) and a new Type-aware Locality-Sensiti Hashing (TLSH) scheme to accomplish it.
['Michael N. Gubanov', 'Anna Pyayt']
Type-aware Web-search