We studied the problem of searching answers for questions on a Question-and-Answer Website from knowledge bases. A number of research efforts had been developed using Stack Overflow data, which is available for the public. Surprisingly, only a few papers tried to improve the search for better answers. Furthermore, current approaches for searching a Question-and-Answer Website are usually limited to the question database, which is usually the website own content. We showed it is feasible to use knowledge bases as sources for answers. We implemented both vector-space and topic-space representations for our datasets and compared these distinct techniques. Finally, we proposed a hybrid ranking approach that took advantage of a machine-learned classifier to incorporate the tag information into the ranking and showed that it was able to improve the retrieval performance.

Library of Congress Subject Headings

Data mining; Information retrieval; Machine learning

Publication Date


Document Type


Student Type


Degree Name

Information Sciences and Technologies (MS)

Department, Program, or Center

Information Sciences and Technologies (GCCIS)


Qi Yu

Advisor/Committee Member

Xumin Liu

Advisor/Committee Member

Rui Li


Physical copy available from RIT's Wallace Library at QA76.9.D343 L46 2016


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