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伴随着Web技术的不断演进与发展,人类先后经历了以文档互联为主要特征的“Web 1.0”时代与数据互联为特征的“Web 2.0”时代,正在迈向基于知识互联的崭新“Web 3.0”时代[1]。知识互联的目标是构建一个人与机器都可理解的万维网,使得人们的网络更加智能化。然而,由于万维网上的内容多源异质,组织结构松散,给大数据环境下的知识互联带来了极大的挑战。因此,人们需要根据大数据环境下的知识组织原则[2],从新的视角去探索既符合网络信息资源发展变化又能适应用户认知需求的知识互联方法[3],从更深层次上揭示人类认知的整体性与关联性[4]。知识图谱(knowledge graph)以其强大的语义处理能力与开放互联能力,可为万维网上的知识互联奠定扎实的基础,使Web 3.0提出的“知识之网”愿景成为了可能。
知识图谱并非是一个全新的概念,早在2006年,文献[5]就提出了语义网的概念,呼吁推广、完善使用本体模型来形式化表达数据中的隐含语义,RDF (resource description framework)模式(RDF schema)和万维网本体语言(Web ontology language,OWL)的形式化模型就是基于上述目的产生的。随后掀起了一场语义网研究的热潮,知识图谱技术的出现正是基于以上相关研究,是对语义网标准与技术的一次扬弃与升华。
知识图谱于2012年5月17日被Google正式提出[6],其初衷是为了提高搜索引擎的能力,增强用户的搜索质量以及搜索体验。目前,随着智能信息服务应用的不断发展,知识图谱已被广泛应用于智能搜索、智能问答、个性化推荐等领域。尤其是在智能搜索中,用户的搜索请求不再局限于简单的关键词匹配,搜索将根据用户查询的情境与意图进行推理,实现概念检索。与此同时,用户的搜索结果将具有层次化、结构化等重要特征。例如,用户搜索的关键词为梵高,引擎就会以知识卡片的形式给出梵高的详细生平、艺术生涯信息、不同时期的代表作品,并配合以图片等描述信息。知识图谱能够使计算机理解人类的语言交流模式,从而更加智能地反馈用户需要的答案[7]。与此同时,通过知识图谱能够将Web上的信息、数据以及链接关系聚集为知识,使信息资源更易于计算、理解以及评价,并且形成一套Web语义知识库。
本文的第一部分将沿着前面叙述,进一步剖析知识图谱的定义与架构;第二部分将以开放链接知识库、垂直行业知识这两类主要的知识库类型为代表,简要介绍其中的几个知名度较高的大规模知识库;第三部分将以知识图谱中的关键技术为重点,详细阐述知识获取、知识表示、知识融合、知识推理技术中的相关研究以及若干技术细节;第四部分将介绍知识图谱在智能搜索、深度问答、社交网络以及垂直行业中的典型应用;第五部分将介绍知识图谱所面临的一些困难与挑战;第六部分将对全文的内容进行总结。
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