  Search has for a long time been an important tool for users to retrieve
information. Syntactic search is matching documents or objects containing
specific keywords like user-history, location, preference etc. to improve the
results. However, it is often possible that the query and the best answer have
no term or very less number of terms in common and syntactic search can not
perform properly in such cases. Semantic search, on the other hand, resolves
these issues but suffers from lack of annotation, absence of WordNet in case of
low resource languages. In this work, we have demonstrated an end to end
procedure to improve the performance of semantic search using semi-supervised
and unsupervised learning algorithms. An available Bengali repository was
chosen to have seven types of semantic properties primarily to develop the
system. Performance has been tested using Support Vector Machine, Naive Bayes,
Decision Tree and Artificial Neural Network (ANN). Our system has achieved the
efficiency to predict the correct semantics using knowledge base over the time
of learning. A repository containing around a million sentences, a product of
TDIL project of Govt. of India, was used to test our system at first instance.
Then the testing has been done for other languages. Being a cognitive system it
may be very useful for improving user satisfaction in e-Governance or
m-Governance in the multilingual environment and also for other applications.
