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对于知识

For knowledge
课程网址: http://videolectures.net/iswc2019_euzenat_for_knowledge/  
主讲教师: Jérôme Euzenat
开课单位: INRIA研究机构
开课时间: 2019-12-10
课程语种: 英语
中文简介:

许多动物都能够从它们的环境中学习,但人类在其中很特别,因为他们可以表达知识并进行交流。文字表达和交流让知识传播摆脱了时间和空间的束缚。它们允许直接从详细的知识中学习,而不是从经验中学习。这些关键特征导致了整个文化的产生,为该物种提供了选择优势。到目前为止,促进文化交流的万维网是这个故事的高潮。

因此,允许机器掌握这些知识的语义网的想法是一个伟大的想法。唉,二十年后,语义网领域主要集中在数据上,即使它是由所谓的知识图谱组成的。当然,也有图式和词汇,但它们只是一种简单的知识。尽管数据可能是开放的,但机器最终学到的知识通常不会被公开,也不会易于交流。这使我们走下了知识进化的阶梯。

必须恢复在网络上正式表达知识的宏伟目标。我们不需要一成不变的知识,而是可以无缝进化的知识;我们不需要建立单一的知识来源,而是鼓励多样性,这是争论和稳健性的来源;我们不需要网络规模的一致知识,而是可以结合的本地理论。我们将特别讨论如何通过从文化进化和进化认识论中汲取灵感,使知识变得生动和进化。

课程简介: A large range of animals are able to learn from their environment, but human beings are special among them because they can articulate knowledge and they can communicate it. Written expression and communication have allowed to get rid of time and space in knowledge transmission. They allow learning directly from elaborated knowledge instead of by experience. These key features have led the creation of whole cultures, providing a selective advantage to the species. The worldwide web facilitating cultural exchange is a culminating point in this story, so far. Hence, the idea of a semantic web allowing machines to have a grasp on this knowledge is a tremendous idea. Alas, after twenty years, the semantic web field is mostly focused on data, even when it is made of so-called knowledge graphs. Of course, there are schemata and vocabularies, but they are only a simple kind of knowledge. Although data may be open, knowledge eventually learnt by machines is very often not disclosed nor prone to communication. This brings us down the knowledge evolution ladder. The grand goal of formally expressing knowledge on the web must be rehabilitated. We do not need knowledge cast in stone for ever, but knowledge that can seamlessly evolve; we do not need to build one single knowledge source, but encourage diversity which is source of disputation and robustness; we do not need consistent knowledge at the web scale, but local theories that can be combined. We will discuss in particular how knowledge can be made live and evolve by taking inspiration from cultural evolution and evolutionary epistemology.
关 键 词: 环境学习; 本地理论; 知识图谱
课程来源: 视频讲座网
数据采集: 2021-06-18:yumf
最后编审: 2021-06-18:yumf
阅读次数: 57