When Search Goes Cognitive

本研究的目标是将搜索、知识图谱、问答和推荐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将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，以建立一个越来越深的知识语料库，从中向用户提供相关信息。提出了本研究工作的三个主题，分别是数据驱动的知识图谱、问答系统和推荐。包括研究生和本科生在内的15名学生将接受培训。博士生和硕士生各负责一个项目，本科生主要关注搜索引擎作为他们的四年级项目或论文。所有方法将在Clueweb12-B13、Amazon、Microblog、Yelp、SemEval等真实数据集上进行评估。原型将在主题的每个阶段进行开发。这项拟议研究的结果将对加拿大、国际企业和政府机构有用。建议的框架可用于金融行业（例如道明银行和股票市场）、医疗保健（例如ICES）、政府机构（例如加拿大统计局）和技术公司（例如亚马逊和b谷歌）。我们的课程将在大数据、人工智能、信息检索和知识发现领域培养学生，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.