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基于实例的本体知识获取

Instance-based ontological knowledge acquisition
课程网址: http://videolectures.net/eswc2013_zhao_knowledge/  
主讲教师: Lihua Zhao; Laura Hollink
开课单位: 数学与信息中心
开课时间: 2013-07-08
课程语种: 英语
中文简介:
链接的开放数据(lod)云包含大量相互链接的实例,从中我们可以检索到丰富的知识。然而,由于存在着异构的、大的本体,手工学习所有的本体是很费时的,很难观察哪些属性对于描述特定类的实例是重要的。为了构建一个能够帮助用户轻松访问各种数据集的本体,我们提出了一个半自动的本体集成框架,该框架可以减少本体的异构性,并检索每个类常用的核心属性。该框架由三个主要部分组成:基于图的本体集成、基于机器学习的本体模式提取和本体合并。该框架通过对链接数据集实例的分析,获取本体知识,构建了一个高质量的集成本体,通过简单的SPARQL查询,可以方便地理解和有效地从各种数据集获取知识。
课程简介: The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology integration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.
关 键 词: 开放数据; 本体集成框架; 关联数据集
课程来源: 视频讲座网
最后编审: 2019-11-30:lxf
阅读次数: 28