When Search Goes Cognitive

本工作的一个目标是将搜索、知识图、问答和推荐结合到异质语料库上的认知搜索中。提出的认知搜索体系结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，以构建越来越深入的知识库，并从中向用户提供相关信息。 本文提出了数据驱动知识图、问答系统和推荐三个主题。包括研究生和本科生在内的15名学生将接受培训。每个项目将有一名博士生和一名硕士生，本科生主要专注于搜索引擎作为他们的四年级项目或论文。所有方法都将在真实数据集上进行评估，如ClueWeb12-B13、亚马逊、微博、Yelp、SemEval等。原型将在这些主题的每个阶段开发。 这项拟议的研究结果将对加拿大、国际企业和政府机构有用。金融行业(例如TD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(例如亚马逊和谷歌)可以使用建议的框架。我们的项目将在大数据、人工智能、信息检索和知识发现领域对学生进行培训，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.