When Search Goes Cognitive

本文的一个目标是将联合收割机搜索、知识图谱、问答和推荐等技术结合到异构语料库的认知搜索中。提出的认知搜索架构是为了将联合收割机强大的索引技术与先进的自然语言处理能力和机器学习算法相结合，以构建一个越来越深的知识语料库，从中向用户提供相关信息。 提出了数据驱动的知识图、问答系统和推荐系统三个研究主题。15名学生，包括研究生和本科生，将接受培训。一个博士每个项目将由一名本科生和一名硕士生完成，本科生主要将搜索引擎作为他们第四年的项目或论文。所有的方法都将在真实的数据集上进行评估，如Cuberweb 12-B13，Amazon，微博，Yelp，SemEval等。原型将在三个主题的每个阶段开发。 这项研究的结果将对加拿大，国际企业和政府机构有用。拟议的框架可供金融业使用（例如，道明银行和股票市场）、医疗保健（例如，海考会）、政府机构（例如，加拿大统计局）和技术公司（例如，Amazon和Google）。我们的计划将在大数据，人工智能，信息检索和知识发现领域培训学生，使他们在申请学术和工业工作时处于有利地位。

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.