With only a few years, the rapid developing Web has led to revolutionary changes in the whole human society. The Semantic Web is the next generation of the Web. It provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. Semantic Web technology facilitates building a large-scale Web of machine-readable knowledge that allows for data reuse and integration. Nowadays, many scholars and experts are devoting them to the work of applying the Semantic Web theories into specific practice, while in turn improving the Semantic Web theories and techniques according to the demand in practice. Web Science involves the full scope of Web-related applications, integrating all directions of Web-related interdisciplinary research into a new field. This joint conference aims to promote expansions from the Semantic Web to Web Science, and to discuss the core technology on the Next Generation Web, including swarm intelligence, semantic search, Web security, natural language processing (NLP) etc.

1. Organization Committee
Conference Chairs:
  Donghong Ji, Wuhan University
  Jeff Z. Pan, University of Aberdeen
Program Committee Chairs:
  Dongyan Zhao, Peking University
  Jianfeng Du, Guangdong University of Foreign Studies
Workshop Chair:
  Zhichun Wang, Beijing Normal University
Publicity Chair:
  Wei Hu, Nanjing University
Publication Chair:
  Zhiqiang Gao, Southeast University
Poster & Demo Chairs:
  Haofeng Wang, East China University of Science and Technology
  Peng Wang, Southeast University
Summer School Chairs:
  Guilin Qi, Southeast University
  Jie Tang, Tsinghua University
Tutorial Chair:
  Huajun Chen, Zhejiang University
Local Chair:
  Jinguang Gu, Wuhan University of Science and Technology
  Siwei Yu, Wuhan University

2. Registration
 Main Conference Fees Only

          Early         Late         On-Site
student CNY1,000 CNY1,500 CNY2,000
Academy CNY1,500 CNY2,000 CNY2,500
industry CNY2,000 CNY2,500 CNY3,000

 Summer School Fees Only  CNY 700

 Main Conference + Summer School Fees

         Early         Late         On-Site
student CNY1,500 CNY2,000 CNY2,500
Academy CNY2,000 CNY2,500 CNY3,000
industry CNY2,500 CNY3,000 CNY3,500

 Important Dates
Early: Before 25th, July 2014.
Late: Before 31st, July 2014
On-Site: From 1st, August to the opening of the conference

 Account Information
Account Name（账户名称）: 武汉科技大学（Wuhan University of Science and Technology）
Account Number（账户号）: 570357550123
Bank of Deposit（开户银行）: 中国银行青山支行（Qingshan Subbranch, Bank of China）

3. Contact information
For further information please contact Fangfang Xu, Email: fannyxff@gmail.com or Cell Phone +86-13995541659

4. Attachment
 Program Schedule
 Conference Map
 Travel & Venue

Program Schedule
1. Main Conference program at a glance
Time theme
11-Aug-14 Monday 8:20-8:50    Registration
                         9:00-9:30    The Opening Ceremony
                         9:40-11:40  Keynote 1: Prof. Dr. Rudi Studer
                                                        Keynote 2: Prof. Dr. Maosong Sun(孙茂松)
                         12:00-14:00 Launch
                         14:00-17:30 Main Track
                         18:00-20:00 Dinner
12-Aug-14 Tuesday 9:00-10:00  Keynote 3: Prof. Dr. Guoren Wang(王国仁)
                         10:00-11:30 Main Track
                         14:00-17:30 Poster & Demo Track, Workshops

Keynote 1: Prof. Dr. Rudi Studer, Karlsruhe Institute of Technology
Bio: Rudi Studer is Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB. In addition, he is director at the Karlsruhe Service Research Institute (KSRI) as well as at the FZI Research Center for Information Technology. His research interests include knowledge management, semantic web technologies and applications, data and text mining, big data and services.
He obtained a Diploma in Computer Science at the University of Stuttgart in 1975. In 1982 he was awarded a Doctor's degree in Mathematics and Informatics at the University of Stuttgart, and in 1985 he obtained his Habilitation in Informatics at the University of Stuttgart. From 1985 to 1989 he was project leader and manager at the Scientific Center of IBM Germany.

He is involved in various national and international cooperation projects, among others the EU Network of Excellence on Large-Scale Data Management (PlanetData) as well as the EU projects XLike (Cross-lingual Knowledge Extraction) and xLiMe (Crosslingual and Crossmedia Knowledge Extraction). He is a member of the advisory board of IEEE Intelligent Systems as well as a fellow of Semantic Technologies Institute International.

Keynote 2: 孙茂松, 清华大学教授
孙茂松，清华大学计算机科学与技术系系主任，教授，博士生导师。研究方向为自然语言理解、中文信息处理和Web智能。作为项目负责人，主持973二级课题、863重大专项二级课题、国家自然科学基金重点项目、国家自然科学基金项目、863项目、国际合作项目等约20项，主持信息处理用分词国际标准2项。在国际刊物、国际会议、国内核心刊物上共发表论文130余篇，获得国家发明专利4项。多次担任相关领域国际会议和全国性学术会议大会主席或程序委员会主席。主要学术兼职为中国中文信息学会副理事长，国务院学位委员会第六届学科评议组计算机科学与技术组成员，国家自然科学基金委员会第十二届专家评审组成员，北京市语言文字工作委员会专家委员会副主任，中国计算机学会理事，全国术语标准化技术委员会委员，中关村开放实验室联盟副理事长，浙江省地税信息化建设专家顾问委员会委员，《中文信息学报》(计算机类全国核心期刊)主编，Journal of Computer Science and Technology、《中国计算机学会通讯》、《计算机科学与探索》、《计算机教育》、《语言文字应用》、《南开语言学刊》、《澳门语言学刊》等期刊编委，863重点项目“中文为核心的多语言处理技术”总体专家组组长等。

Keynote 3: 王国仁, 东北大学教授
王国仁，博士、教授、博士生导师、长江学者特聘教授、国家杰出青年科学基金获得者、国家自然科学基金委信息学部专家评审组成员、中国计算机学会数据库专业委员会副主任委员。 分别于1988年、1991年和1996年获得东北大学计算机专业学士、硕士和博士学位。 现任东北大学科学技术处处长，信息科学与工程学院计算机系统研究所所长。
主持国家杰出青年科学基金、国家自然科学基金重点项目和面上项目、国家863计划项目等20余项。 获得国家科技进步二等奖、辽宁省科技进步一等奖、教育部自然科学二等奖、辽宁省自然科学二等奖等省部级科学技术奖励共9项。指导的博士研究生获得国家百篇优秀论文提名奖1篇、辽宁省优秀博士论文2篇、中国计算机学会优秀论文1篇。 发表学术论文100余篇，主要包括IEEE Transactions on Knowledge and Data Engineering、 IEEE Transactions on Parallel and Distributed Systems、IEEE Transactions on Systems, Man, and Cybernetics、ACM Transactions on Internet Technology等顶级学术期刊和SIGMOD、VLDB、ICDE等顶级学术会议。主要研究方向包括：不确定数据管理、数据密集型计算、可视媒体数据管理与分析、非结构化数据管理、分布式查询处理与优化技术（主要包括传感器网络和P2P对等计算）、生物信息学等。

2. Summer School & Tutorial Program
Time Title Invited Speaker
8-Aug-14
 9:00-12:00  Semantic search on linked data-Part one Haofeng Wang(王昊奋)
 14:00-16:30 Ontology-based data access Guohui Xiao
9-Aug-14
 9:00-12:00  Semantic search on linked data-Part two Haofeng Wang(王昊奋)
 14:00-16:30 互联网广告中的匹配和排序算法 蒋龙
10-Aug-14
 9:00-12:00  百度知识图谱的建设及其在深度问答中的应用 马艳军
 14:00-16:30 Tutorial:
BigData: Hashing Algorithms for Large-Scale Search, Learning, and Compressed Sensing  Ping Li

Titile: Semantic search on linked data
Dr. Haofeng Wang, East China University of Science & Technology
Abstract: Semantic Web is to add explicit semantics for data so that machines can not only display these data, but also understand, process and even integrate them. In recent years, with the fast development of the Linking Open Data (LOD) project and the DBpedia project, Semantic Web data sources increase significantly and a large number of graph data in form of RDF are published. The Web is rapidly changing from the one containing Web pages and hyper-links only (called Document Web) to a Data Web with abundant entities and rich relationships between entities. Large search engine companies like Google are building knowledge graph based on these semantic data to improve the quality of search, which indicates that we enter the era of semantic search. In this talk, I will first introduce LOD, the principle and best practice to publish linked data as well as the fundamental of RDF data model and SPARQL query language. Then I will focus on semantic search on LOD. In particular, I will introduce the underlying technologies of several popular semantic search engines such as Sindice, Sig.ma, Falcons, Watsons, Semplore and Hermes.

Bio: Dr. Wang Haofen graduated from Shanghai Jiao Tong University in 2013. Now he is an assistant professor in East China University of Science & Technology. Dr. Wang has published more than 40 papers in CCF A-level conferences like SIGMOD, SIGIR and AAAI, and CCF B-level conferences like ISWC, CIKM and WWW. He has also published 3 papers in Journal of Web Semantics (CCF B-level journal with impact factor as 3.049), 1 paper at ACM Transaction on Intelligent Systems and Technology, and 1 paper in IEEE Computational Intelligence (impact factor as 2.833). As a technical leader, he led a team to build a scalable semantic search engine, which won the 2nd price of Billion Triple Challenge, ISWC 2008. He also led a team to won the 1st price of data interlinking in ontology matching competition, OAEI, 2011. Moreover, He built the first effort of Chinese Linked Open Data: zhishi.me and was invited to give a talk at W3C Multilingual Workshop, 2013.

Title: Ontology-based data access
Prof. Dr. Guohui Xiao, Free University of Bozen-Bolzano, Italy
Abstract: In this tutorial we provide a comprehensive understanding of the problem of ontology-based data access (OBDA), from both thetheoretical and the practical points of view. In OBDA, the objective is to access data trough a conceptual layer.Usually, this conceptual layer is expressed in the form of an OWL or RDFS ontology, and the data is stored in relational databases. The terms in the conceptual layer (RDF Graph) are mapped to the data layerusing so-called global-as-view (GAV) mappings, associating to each element of the conceptual layer a (possibly complex) query over thedata sources. GAV mappings have been formalized in the recent R2RML W3C standard. This virtual graph can then be queried using an RDFquery language such as SPARQL.The advantages of OBDA are two folds: (1) with OBDA, one can access the(legacy) database using semantic web techniques; (2) by reusing the techniques in the database system, OBDA provides scalable performance.we provide also a "hands-on'' experience with Ontop, a state-of-the-art system for OBDA. Ontop is an open-source project released under Apache License, developed at the Free University ofBozen-Bolzano and part of the core of the EU project Optique. Ontop is available as: a plugin for Protege 4, SPARQL end-point, and OWLAPI and Sesame libraries. To the best of our knowledge, Ontop is the first system supporting all the following W3C recommendations: OWL, R2RML,SPARQL, SWRL and SPARQL OWL 2 QL regime.
Bio: Dr. Guohui Xiao is a Postdoc researcher at the KRDB Research Centre for Knowledge and Data of Free University of Bozen-Bolzano. Hereceived his PhD from Vienna University of Technology, Austria in 2014 and his MSc and BSc degrees from Peking University, China. His research interests include knowledge representation and reasoning, semantic web, ontology-based data access, and non-monotonic reasoning. He has published several papers in these areas, many of which are published in proceedings of major conferences and journals. He is currently working in the EU FP7 Optique project (Scalable End-userAccess to Big Data) and is one of the core developers of the Ontop system for OBDA.

Title: 百度知识图谱的建设及其在深度问答中的应用
马艳军博士, 百度
Bio: 马艳军博士现为百度公司自然语言处理部研究员，从事知识图谱、智能问答等方面的研发工作。2009年获得爱尔兰都柏林城市大学计算机系博士学位，曾在ACL, COLING, ACM Transaction, Journal of Machine Translation等会议和期刊发表论文多篇，拥有发明专利多项，曾负责多项爱尔兰和欧盟FP7项目的子课题。2008年至2009年担任EACL学生委员会理事，2009年在剑桥大学工程系做访问学者，2010年获得欧洲机器翻译协会最佳博士论文奖。现担任Journal of Machine Translation编委，并担任ACL, EMNLP, COLING等国际会议的PC member、area chair、session chair等。

Title:互联网广告中的匹配和排序算法
蒋龙博士，通联数据
Abstract: 互联网广告是利用互联网提供的基础设施进行产品和服务营销的一种新形式，具有比传统广告方式更精准，成本收益更透明的优势。互联网广告是当今众多互联网企业实现价值变现的最主要形式之一，可以说，互联网广告产业是当今互联网能蓬勃发展的重要动力。
本次讲座将首先对互联网广告产业做一个简要的介绍，包括目前主流的几种广告模式，每种模式下主要的参与者及广告形态和特点。接下来分析几种主流广告模式中应用到的匹配和排序技术，包括搜索广告系统中的查询匹配算法，定向广告中的行为定向和个性化推荐技术，以及基于反馈数据的点击率预估系统。
Bio: 蒋龙，毕业于北京大学，现任通联数据首席数据科学家。曾任职于阿里巴巴集团，负责阿里妈妈事业部机器学习和排序，推荐和用户模型，基础算法和数据等团队。加入阿里之前曾任职微软亚洲研究院，从事自然语言处理、机器学习研究工作。主要感兴趣的领域包括机器学习，自然语言处理，互联网广告，量化投资等。

Title: BigData: Hashing Algorithms for Large-Scale Search, Learning, and Compressed Sensing
Prof. Dr. Ping Li, Department of Computer Science, Rutgers University
Bio: Ping Li is Associate Professor in the Department of Statistics and the Department of Computer Science at Rutgers University. He graduated from Stanford University with Ph.D. in Statistics (plus two Master's degrees in CS and EE). Ping Li's research interests include probabilistic hashing algorithms for big data, information retrieval, boosting, data streams, and compressed sensing. He has been publishing extensively in premier venues in data mining, machine learning and theory including WWW, NIPS, UAI, ICML, KDD, SODA, COLT, etc. Ping Li's research has been funded by the Department of Defense, Microsoft, Google, and the National Science Foundation (NSF). In particular, he was one of the PIs of the recent NSF-Bigdata program. Ping Li received the Young Instigator Award (YIP) from the Air Force Office of Scientific Research (AFOSR) and YIP from the Office of Naval Research (ONR). He also won a prize in 2010 Yahoo! Learning to Rank Grand Challenge using own boosting/tree algorithms.