  Semantic search technology has received more attention in the last years.
Compared with the keyword based search, semantic search is used to excavate the
latent semantics information and help users find the information items that
they want indeed. In this paper, we present a novel approach for semantic
search which combines Multi-Categorization Semantic Analysis with
personalization technology. The MCSA approach can classify documents into
multiple categories, which is distinct from the existing approaches of
classifying documents into a single category. Then, the search history and
personal information for users are significantly considered in analysing and
matching the original search result by Term Vector DataBase. A series of
personalization algorithms are proposed to match personal information and
search history. At last, the related experiments are made to validate the
effectiveness and efficiency of our method. The experimental results show that
our method based on MCSA and personalization outperforms some existing methods
with the higher search accuracy and the lower extra time cost.
