When Search Goes Cognitive

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A goal of this work is to combine search, knowledge graph, question answering and recommendation to cognitive search on heterogeneous corpus. The proposed cognitive search architecture is presented to combine powerful indexing technology with advanced natural language processing capabilities and machine learning algorithms to build an increasingly deep corpus of knowledge from which to feed relevant information to users.  Three themes for this research work are proposed, namely data-driven knowledge graph, question answering system and recommendation respectively. 15 students, including both graduate and undergraduate students, will be trained. One Ph.D. student and one master's student will work on each project, and the undergraduate students mainly focus on search engines as their fourth-year projects or theses. All methods will be evaluated on real data sets such as Clueweb12-B13, Amazon, Microblog, Yelp, SemEval etc. Prototypes will be developed at every stage of the there themes. The results of this proposed research will be useful for Canadian, international businesses and government institutions. The proposed frameworks can be used by finance industry (e.g., TD Bank and stock market), healthcare (e.g., ICES), governmental institutions (e.g., Statistics Canada), and technological companies (e.g., Amazon and Google). Our program will train students in big data, artificial intelligence, information retrieval and knowledge discovery areas to place them in a strong position when applying for academic and industrial jobs.