近年来,深度学习技术依托强大的计算资源、复杂的神经网络和大规模的标注数据集在视觉、语言、医学、金融等广泛的研究领域取得了显著的成就。然而,在现实应用场景中,尤其是在开放世界的背景假设下,随时会有一些新的概念/对象出现,持续地为这些新概念/对象收集样本并进行标注是极为不现实的。因此,致力于解决深度学习模型在没有标注数据条件下学习和预测问题的零样本学习方法(Zero-shot Learning, ZSL)被提出并引起广泛的研究兴趣,有效地缓解了现有深度学习模型尤其是有监督学习模型对标注数据(即训练样本)的依赖。
ZSL系列算法的有效实施离不开额外的语义知识,这些知识对所预测的对象在除样本之外的维度进行了描述,且相比于标注样本更容易获取,从而帮助模型进行有效的特征迁移并完成推理预测。典型的语义知识如文本描述、属性标注,此外,知识图谱及本体在该领域也被广泛应用。不同类型的语义知识,其获取难度、表达能力以及对于ZSL模型的增益都有着很大区别,因此,在本年度国际语义网大会上,我们组织了讲习班报告,从知识的视角,特别是知识图谱的视角,对零样本学习技术相关的概念、技术路线和数据资源进行详细的介绍,同时准备相关demo算法供听众学习,希望以这类知识驱动的零样本学习技术为出发点,探究“机器认知”+“知识感知”、“神经”+“符号”的有效集成模式,从而推动人工智能系统的进步。
在本次报告中,我们将为各位听众带来:
•0基础了解零样本学习技术背景、多维度垒石知识图谱基础
•知识驱动的零样本学习主要技术路线及方法
•多领域开放数据资源、多案例demo算法快速入门
Jiaoyan Chen is a Senior Researcher at Department of Computer Science, University of Oxford, working in the Knowledge Representation and Reasoning Group led by Prof. Ian Horrocks. In past ten years, he has been working on KG construction and curation technologies, knowledge-aware machine learning (including KG-based low-resource learning, inductive KG completion, concept drift in ontology stream learning, knowledge-augmented explanation), as well as the applications of these semantic and machine learning technologies.
Yuxia Geng is a PhD student in the Knowledge Engine Group at Zhejiang University, advised by Prof. Huajun Chen. Her research interests include KG-aware ZSL, inductive KG representation and reasoning, and Neuro-symbolic Integration. She has been contributing to Knowledge-driven ZSL, with a few papers published in Journal of Web Semantics, Semantic Web Journal, KDD, WWW, ACL, IJCAI, ISWC, and KR as a main author.
Yufeng Huang is a MSc student in the Knowledge Engine Group at Zhejiang University, advised by Prof. Huajun Chen. His research interests include KG, ZSL and visual language models. He is familiar with kinds of machine learning and KG coding skills, and mainly contribute to the hands-on session.
Huajun Chen is a full professor of College of Compouter Science and Technology at Zhejiang University. He has over 20 years research experience on topics such as the Semantic Web, KG, low-resource learning, Big Data and NLP. His research on KG has won several international and national awards, such as the best paper of ISWC 2006. Prof. Chen serves as the Area Editor of Journal of Web Semantics, and leads OpenKG, an open KG initiative launched in 2015. Huajun has been teaching in Zhejiang University for over 15 years for courses such as Introduction to Knowledge Graph and Object Oriented Programming.
International Semantic Web Conference (ISWC) 是语义网和知识图谱领域的国际顶级学术会议,自2002年第一届会议举办以来,20年间,全世界相关的科研工作者、从业者和领域专家,在会议上共同探讨、推进、塑造知识图谱与语义网技术未来。2022年,第21届ISWC会议将通过线上的方式于10月23至27日期间举办。
ISWC 2022 会议官网:https://iswc2022.semanticweb.org/
2022年10月23日
北京时间:16:00-20:00
欧洲中部夏令时:10:00-14:00
北美东部夏令时:04:00-08:00
我们有众多非常出色的研究人员参与了我们在零样本学习和知识图谱方面的工作,并对我们的Tutorial提出了宝贵的建议,包括来自爱丁堡大学的Jeff Z. Pan博士,来自浙江大学的张文博士和陈卓同学,来自牛津大学的Ian Horrocks教授等等。
点击阅读原文,进入讲习班页面。
OpenKG
OpenKG(中文开放知识图谱)旨在推动以中文为核心的知识图谱数据的开放、互联及众包,并促进知识图谱算法、工具及平台的开源开放。