本论坛将于CNCC2019中国计算机大会第一天（10月17日）下午在苏州金鸡湖国际会议中心A305会议室举行，届时我们将和多位认知计算与知识工程领域的著名研究者一起探讨如何通过半自动化的交互学习构建超大规模认知图谱，并探讨其在实际系统中的可能应用。
知识引擎是人工智能的核心和基础设施。 大数据环境下数据的分布、异构、动态、碎片化和低质等特征给知识工程和知识服务提出了新挑战，既需要从感知角度学习数据的分布表示，又需要从认知角度解释数据的语义，构建新一代知识图谱，暨认知图谱成为实现人工智能的关键。
2019年中国计算机大会将于10月17日-19日在苏州金鸡湖国际会议中心举行。大会期间将进行多场内容丰富的论坛活动。其中“认知图谱与推理”技术论坛将于2019年10月17日下午举行。本次论坛将再次聚焦新一代知识图谱的发展，深入讨论机器学习和符号计算相结合的知识表示、获取、推理和知识服务理论和方法。
本次论坛由清华大学计算机系唐杰教授担任主席，阿里巴巴达摩院资深算法专家、International Statistical Institute和中国电子学会青年科学家俱乐部理事杨红霞担任共同主席。论坛将邀请包括清华大学计算机系教授李涓子，阿里巴巴资深总监、通用机器学习平台PAI负责人林伟，阿里巴巴总监，淘宝反作弊业务负责人李朝等在内的多位认知计算与知识工程领域的著名研究者一起探讨如何通过半自动化的交互学习构建超大规模认知图谱，并探讨其在实际系统中的可能应用。
去年，由计算机学会自然语言处理专委会在CNCC上组织的“认知图谱与推理”技术论坛邀请了6位认知计算与知识工程领域的著名研究者，包括Microsoft Research Outreach的常务董事Kuansan Wang、阿里巴巴资深算法专家Hongxia Yang、北京大学研究员赵东岩、阿里巴巴计算平台工程总监钱正平、南京大学教授黎铭、智慧金融研究院科学顾问杨洋。活动吸引了众多学者和业界相关人士到场参与，现场十分火爆。
2018年“认知图谱与推理”技术论坛现场
今年“认知图谱与推理”技术论坛将继续关注认知图谱与推理的机遇与挑战，探讨如何推动认知图谱与推理的发展，增进领域学者间的交流与互动，使参加者在了解学科专题基础、提高理论水平的同时，掌握本领域最新技术动态，了解未来技术趋势。
主席 唐杰，清华大学计算机系教授
清华大学计算机系教授、系副主任，获杰青。研究兴趣包括：数据挖掘、社交网络和知识图谱。发表论文200余篇，引用10000余次。主持研发了研究者社会网络挖掘系统AMiner，吸引全球220个国家/地区1000多万独立IP访问。曾担任国际期刊ACM TKDD的执行主编和国际会议CIKM’16、WSDM’15的PC Chair、KDD’18大会副主席。作为第一完成人获北京市科技进步一等奖、中国人工智能学会科技进步一等奖、KDD杰出贡献奖。
共同主席 杨红霞，阿里巴巴达摩院资深算法专家
杜克大学博士。拥有顶级论文30余篇。曾任IBM Watson研究员、Yahoo！主任数据科学家等职。目前致力于研发新一代结合超大规模知识图谱和图计算的推理系统。
报告人：杨红霞，阿里巴巴达摩院资深算法专家
讲者简介：杜克大学博士。拥有顶级论文30余篇。曾任IBM Watson研究员、Yahoo！主任数据科学家等职。目前致力于研发新一代结合超大规模知识图谱和图计算的推理系统。
报告题目：AliGraph: A Comprehensive Graph Neural Network Platform
报告摘要：An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relation- ship among potentially billions of elements. Graph Neural Network (GNN) becomes an effective way to address the graph learning problem by converting the graph data into a low dimensional space while keeping both the structural and property information to the maximum extent and constructing a neural network for training and referencing. However, it is challenging to provide an efficient graph storage and computation capabilities to facilitate GNN train- ing and enable development of new GNN algorithms. In this paper, we present a comprehensive graph neural network system, namely AliGraph, which consists of distributed graph storage, optimized sampling operators and runtime to efficiently support not only exist- ing popular GNNs but also a series of in-house developed ones for different scenarios. The system is currently deployed at Alibaba to support a variety of business scenarios, including product recommendation and personalized search at Alibaba’s E-Commerce platform. By conducting extensive experiments on a real-world dataset with 492.90 million vertices, 6.82 billion edges and rich attributes, Ali- Graph performs an order of magnitude faster in terms of graph building (5 minutes vs hours reported from the state-of-the-art PowerGraph platform). At training, AliGraph runs 40%-50% faster with the novel caching strategy and demonstrates around 12 times speed up with the improved runtime. In addition, our in-house developed GNN models all showcase their statistically significant superiorities in terms of both effectiveness and efficiency (e.g., 4.12%–17.19% lift by F1 scores).
报告人：唐杰，清华大学计算机系教授
讲者简介：清华大学计算机系教授、系副主任，获杰青。研究兴趣包括：数据挖掘、社交网络和知识图谱。发表论文200余篇，引用10000余次。主持研发了研究者社会网络挖掘系统AMiner，吸引全球220个国家/地区1000多万独立IP访问。曾担任国际期刊ACM TKDD的执行主编和国际会议CIKM’16、WSDM’15的PC Chair、KDD’18大会副主席。作为第一完成人获北京市科技进步一等奖、中国人工智能学会科技进步一等奖、KDD杰出贡献奖。
报告题目：认知图谱：知识表示、推理与生成
报告摘要：认知图谱是下一代知识图谱，其主要特点既包括传统的知识图谱构建（如：实体抽取和实体关系挖掘），也涵盖了基于认知理论的推理，还包括基于知识图谱的知识生成。知识表示主要研究基于图神经网络的知识表示和关系挖掘，知识推理探索基于认知和深度学习相结合的推理方法和推理引擎，知识生成则着重探索基于知识图谱的新知识生成等方法。
报告人：Kuansan Wang，Managing Director
讲解简介：Kuansan Wang is a Managing Director of Microsoft Research Outreach, responsible for Microsoft Academic Services. He is also a principal researcher of MSR Redmond, conducting research in web search, data mining, natural language processing and information retrieval. He joined MSR Speech technology group in 1998, working on acoustic modeling, language modeling and spoken and multimodal systems. He has contributed to numerous Microsoft speech products as well as many international standards in the speech areas. He started working on web search in 2007 and became one of the earliest members in the newly created Internet Service Research Center (ISRC). Dr. Wang received his PhD and MS from University of Maryland, College Park, and BS from National Taiwan University, all in Electrical Engineering.
报告题目：学术图谱中的认知问题
报告摘要：我将介绍微软学术搜索中知识图谱的构建方法和技巧，并探讨其中碰到的认知问题。
报告人：李朝，阿里巴巴总监，淘宝反作弊业务负责人
讲解简介：Dr. Zhao Li is currently a senior staff data scientist at Alibaba Group, specializing in adversarial intelligence with attention to e-commence ranking and recommendation systems. He previously worked at TCL Research America as a chief data scientist on recommendation systems. He received a fellowship from NSF and completed a Ph.D. with graduate award in Computer Science Department from University of Vermont. Prior to that, he obtained his M.S. from South China University of Technology. He has published over 50 papers in prestigious conferences including NIPS, IJCAI, AAAI, KDD, etc,. His current research interests include Adversarial Machine Learning, Network Representation Learning, Multi-Agent Reinforcement Learning, and Big Data Driven Security. He is a Technical Committee member of CCF on database. Has has served on program committees of conferences, including AAAI, AAMAS, CIKM, and ICME.
报告题目：Large-Scale Hierarchical Taxonomy via Graph based Query Coalition
报告摘要：E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platforms organizes items into an ontology structure. However, the ontology-driven approach is subject to costly manual maintenance and often does not capture user's search intention, particularly when user searches by her personalized needs rather than a universal definition of the items. Observing that search queries can effectively express user's intention, we present a novel large-Scale Hierarchical taxOnomy via grAph based query coaLition (SHOAL) to bridge the gap between item taxonomy and user search intention. SHOAL organizes hundreds of millions of items into a hierarchical topic structure. Each topic that consists of a cluster of items denotes a conceptual shopping scenario, and is tagged with easy-to-interpret descriptions extracted from search queries. Furthermore, SHOAL establishes correlation between categories of ontology-driven taxonomy, and offers opportunities for explainable recommendation. The feedback from domain experts shows that SHOAL achieves a precision of 98% in terms of placing items into the right topics, and the result of an online A/B test demonstrates that SHOAL boosts the Click Through Rate (CTR) by 5%. SHOAL has been deployed in Alibaba and supports millions of searches for on-line shopping per day.
报告人：Huan Liu 教授，ACM/IEEE Fellow
讲解简介：Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in real-world applications with high-dimensional data of disparate forms. He co-authored the textbook on Social Media Mining: An Introduction by Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, and Chief Editor of Data Mining and Management in Frontiers in Big Data. He is a Fellow of ACM, AAAI, AAAS, and IEEE.
报告题目：The Need for Knowledge in Machine Learning in the Age of Big Data
报告摘要：Big data is pervasive and getting big. The unparalleled computing power combined with big data has shown unprecedented success in many applications. In the awe and praise of the unexpected effectiveness of big data, we seem to forget about the role of knowledge in machine learning, which played an important role in the early days of AI. In this talk, we look at the need for knowledge in our endeavor to go beyond the state-of-art machine learning through case studies and hope to pique the curiosity of using knowledge in machine learning as an external source to constrain the hypothesis space and as a means to reuse the rich results of machine learning in many successful applications.
