Websites such as Scopus, Web of Science, and Google Scholar provide thousands of scholarly information from publishers worldwide. Those websites make basic search on scholar journals of all fields possible. However, the substantial amounts of publication information bring both pros and cons to the people who visit the website. Knowledge can be gained but too much information makes people become indecisive. The scenario has become worse when the user needs to pick one desired result out of the thousands.

Hence, in USM, an existing Expert Search system has narrowed down the scope and only display the publication information of experts from School of Computer Science. Nevertheless, there are still almost 60 expert profiles and each of them published numerous articles every year. A significant amount of time is needed to browse through everything. Students and academic staff are having problems when choosing the most suitable expert to be the supervisor or examiner, respectively.

Therefore, an intelligent conversational tool is much needed to tackle the issues. It can be considered as a new feature added to the existing CS Expert Search engine. Chat Assistant developed in the project facilitates the users from School of Computer Science of USM (both students and academic staff) in decision making by solving all the queries regarding the experts and their publications information. A few techniques will be used to build the chatbot such as natural language processing, mapping of natural language query to structured query and data aggregation. Queries regarding the publication and expert information from School of Computer Science will be treated as the input of the system and the chatbot will respond to the queries in natural language form by delivering accurate and related information to the users.