时间：12月22日13:00-15:00
报告人：英国阿伯丁大学Jeff Z. Pan教授（英籍华人）
地点：院系楼201会议室
主题：知识图谱构建与应用
流程：
13:00-14:00: 与Jeff Z. Pan教授自由交流
14:00-15:00: Jeff Z. Pan给大家做一个关于知识图谱构建与应用的讲座
15:00-15:30: 就讲座相关内容展开问答、深入学术交流
Jeff Z. Pan教授简介（http://homepages.abdn.ac.uk/jeff.z.pan/）：
Jeff Z. Pan received his Ph.D. in computer science from The University of Manchester in 2004, on the topic of Description Logics: Reasoning Support for the Semantic Web. He joined the faculty in the Department of Computing Science at University of Aberdeen in 2005. He is now the Deputy Director of Research of the department. His research focuses primarily on knowledge representation and reasoning, in particular scalable ontology reasoning, querying and reuse, and their applications (such as Semantic Web, Advertising, Healthcare, Software Engineering and Multimedia). He is a key contributor to the W3C OWL2 standard. He leads the work of the TrOWL Tractable OWL2 reasoning infrastructure. He is widely recognised for his work on scalable and efficient ontology reasoning; he gave tutorials on this topic in e.g. AAAI2010, ESWC2010, ESWC2011, SemTech2011 and the Reasoning Web Summer School (2010 and 2011).
Dr Pan is a member of the Advisory Committee in the World Wide Web Consortium (W3C), an international organisation for setting up Web standards such as HTML, RDF and OWL. He is a Primary Member Representative of Object Management Group (OMG), a leading proponent of Business-IT integration standards. He chairs the OWL task force on uncertainty reasoning. He also chaired the W3C Multimedia Semantics Incubator Group and the Software Engineering Task Force in the Semantic Web Best Practice and Deployment (SWBPD) Working Group. His group is currently actively contributing to several W3C technical groups, including the OWL, SPARQL, Provenance and RDB2RDF. He co-edited a number of W3C technical reports, such as "XML Schema Datatypes in RDF and OWL". He is a Board Member of Semantic Technology Institute International (STI2).
Dr. Pan has over 100 refereed publications. He and his colleagues won the Best Paper Award in the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2006) and the Best Student Paper Award in the 5th International Semantic Web Conference (ISWC2006). He served/serves as Program Chair of the First International Conference on Web Reasoning and Rule System (RR2007, which is the first Semantic Web conference on Reasoning), of the Ontology and Reasoning Track in the Extended Semantic Web Conference (ESWC2010) and of the Doctoral Consortiums in the 9th International Semantic Web Conference (ISWC2010) and in the 8th Extended Semantic Web Conference (ESWC2011).
Dr. Pan is a founding member of the International Conference on Web Reasoning and Rule System and the Joint International Conference on Semantic Technologies. He co-founded the Fuzzy RuleML Technical Group. He is an Editor of the International Journal on Semantic Web and Information Systems (IJSWIS), an Aear Editor of the Journal of Web Semantics (JWS) and is on the Editorial Board of the journal of Big Data Research. He serve/served as a guest editor of special issues of a number of international journals, such as the Journal of Web Semantics, the Journal of Logic and Computation, the Journal on Data Semantics and IEEE Transactions on Systems, Man and Cybernetics Part C (Applications and Reviews).
Dr. Pan is the coordinator of the EU IAPP K-Drive project. He has contributed to several high-profile projects, such as the highly prestigious, EPSRC-funded Interdisciplinary Research Collaboration on Advanced Knowledge Technologies (AKT) and the European Commission FET project Wonder Web, which effectively contributed to the creation of the W3C OWL Web Ontology Language. He has also led The Aberdeen University's contribution to several EU-funded and national projects, such as the European Commission Network of Excellent Knowledge Web project, the MOST project and the K-Drive project.
知识图谱简介：知识图谱(Knowledge Graph)是当前的研究热点。自从2012年谷歌(Google)推出自己第一版知识图谱以来，它在学术界和工业界掀起了一股热潮。美国的微软必应，中国的百度、搜狗等搜索引擎公司在短短的一年内纷纷宣布了各自的“知识图谱”产品，如百度“知心“、搜狗“知立方“等。简单地说，知识图谱本质上是语义网络，是一种基于图的数据结构，主要是用来优化现有的搜索引擎。在当今大数据时代，搜索引擎已经成为人们遨游网络信息海洋的不可或缺的工具。但是，传统搜索引擎的工作方式表明，它只是机械地比对查询词和网页之间的匹配关系，并没有真正理解用户要查询的到底是什么。比如，我们在百度上搜索“泰山”，它会尝试将这个字符串与百度抓取的大规模网页作对比，根据网页与这个查询词的相关程度，以及网页本身的重要性，对网页进行排序，作为搜索结果返回给用户。而用户所需的与“泰山”相关的信息，就还要他们自己动手，去访问这些网页来找了。而知识图谱则会将“泰山”理解为一个“实体”，也就是一个现实世界中的事物。这样，搜索引擎会在搜索结果的右侧显示它的基本资料，例如地理位置、海拔高度、别名，以及百科链接等等，此外甚至还会告诉你一些相关的“实体”，如嵩山、华山、衡山和恒山等其他三山五岳等。当然，用户输入的查询词并不限于对应一个实体。例如当在谷歌中查询“apple”（苹果）时，谷歌不单展示IT巨头“Apple-Corporation”（苹果公司）的相关信息，还会在其下方列出“apple-plant”（苹果-植物）的另外一种实体的信息。很明显，知识图谱能为查询词赋予丰富的语义信息，建立与现实世界实体的关系，从而帮助用户更快找到所需的信息。
