学术会议 | 欢迎注册参加第11届国际知识图谱联合会议

2022 年 10 月 21 日 开放知识图谱

11^th International Joint Conference On Knowledge Graphs
(IJCKG 2022)

IJCKG(International Joint Conference On Knowledge Graphs)会议之前是国际语义技术联合会议（the Joint International Semantic Technology Conference (JIST)），JIST 会议的历史要追溯到 2006 年在北京召开的第 1 届亚洲语义网会议 ASWC，第 5 届 ASWC2011 在杭州召开时更名为第 1 届国际语义技术联合会议JIST 2011，8 年后 JIST 2019 再次回到杭州。JIST会议是语义网、知识图谱、关联数据和人工智能主题方面在亚洲召开的高端论坛会议。在2021年，JIST会议和ACM/SIOGAI联合举办，更名为IJCKG，扩大了会议的国际影响力。同时，Data Intelligence在2021年被EI数据库收录后，有幸成为大会推荐期刊，大会优秀论文将推荐在DI知识图谱专辑上发表。

今年IJCKG会议将与ISWC联合举办。于2022年10月27-28日，通过线上的方式举办，IJCKG 2022的使命是将知识图谱社区和其他相关领域的国际研究人员聚集在一起，展示他们的创新研究成果或知识图谱的新应用。期待您的参与！

大会官网：https://www.ijckg.org/2022/

Keynote Speakers

Title: Semantically Controlled Response Generation for Task-Oriented Dialogue Systems

Speaker: Prof. Maarten de Rijke, Distinguished University Professor of Artificial Intelligence and Information Retrieval at the University of Amsterdam, NL

Abstract: Access to information is increasingly conversational in nature. This is especially true in task-oriented settings. Response generation is a core component that translates system actions into natural language responses. In the talk I will discuss some of the challenges of balancing (i) high precision and high predictability of responses that optimize for utility and (ii) diverse and natural responses that optimize for the user experience. I will also discuss some recent advances on architectures that generate system responses by learning to paraphrase semantically constrained template-based responses.(Based on joint work with Phillip Lippe, Pengjie Ren, Hinda Haned, and Bart Voorn)

Title: Introduction to Enterprise knowledge graph Practice and Key Technologies

Speaker: Dr. Baoxing Huai, Vice-director of Huawei Cloud Language and Speech Innovation Lab and Chief architect of Huawei Knowledge Computing

Abstract: The development of digital transformation has driven the application of AI in the industry from perceptual intelligence to cognitive intelligence. Industry and enterprise knowledge inherent in the ocean of unstructured and semi-structured data is valuable for enterprises. The capability of mining and utilizing knowledge from those data is critical for enterprise to achieve knowledge-based transformation. By abstracting the knowledge graph construction process and bonding AI techonologies such as information extraction and KBQA algorithms, an end-to-end knowledge graph solution can be established easily to promote the application of knowledge graphs in different bussiness domains and scenarios. In this presentation, we are going to introduce the exploration and practice of knowledge computing in enterprises, including enterprise-level knowledge graph platform and knowledge extraction. In addition, we will also share the application cases of knowledge graph technology in the industry, and discuss the future technical challenges and trends of enterprise knowledge transformation.