智能制造领域多模态知识图谱数据集

"大数据背景下，网络信息量暴涨，信息来源的分布非常广泛，信息层次的复杂度也越来越高。在广泛的信息来源中，分布着针对同一主题的同一个描述目标的不同或者相同侧面的相关信息，这些信息在表现形式、语义信息、侧重点等方便都不尽相同，这些信息构成了现有的海量异构多源多模态的数字化资源，而这些多模态内容资源普遍存在表现形式多样化、存储容量庞大、元数据标准规范不统一等特点，以至于用户能够获取到的信息往往是分散、单模态的，随着网络多模态内容资源量的增加，用户的信息需求与多模态内容资源的关联利用之间的矛盾加深。 为了满足用户对于海量多模态服务内容资源的细粒度管理和智能化服务的需求，本课题针对文本、图像、语音、视频等多模态内容资源的一致性知识表达机理和面向复杂需求的自适应知识服务重组机制等问题开展研究，并通过语义特征层面的相关性度量技术，实现跨媒体内容间的可解释量化关联，有效解决了用户的信息需求与多模态内容资源高效合理利用之间的矛盾。 本课题针对以上需求，针对每一个多模态数据间构建关联关系，并将使用图数据库neo4j进行数据存储，以及实现查询、更新等常见的数据操作。"Against the backdrop of big data, the volume of online information has exploded, with widely distributed information sources and increasingly complex information hierarchies. Among these extensive information sources, there exists relevant information covering different or identical aspects of the same descriptive target for a single topic. These pieces of information vary in terms of presentation forms, semantic information, focus and other aspects, and constitute the existing massive heterogeneous, multi-source, multi-modal digital resources. Such multi-modal content resources generally feature diversified presentation forms, huge storage capacity, and lack of unified metadata standards and specifications. As a result, the information accessible to users is often scattered and single-modal. With the increasing volume of online multi-modal content resources, the contradiction between users' information needs and the associated utilization of multi-modal content resources has intensified. To meet users' demands for fine-grained management and intelligent services of massive multi-modal service content resources, this research focuses on issues such as the consistent knowledge representation mechanism of multi-modal content resources including text, images, speech and video, and the adaptive knowledge service recombination mechanism for complex demands. By leveraging correlation measurement technology at the semantic feature level, it realizes interpretable quantitative correlation across cross-media content, effectively resolving the contradiction between users' information needs and the efficient and rational utilization of multi-modal content resources. In response to the above demands, this research constructs association relationships between each pair of multi-modal data, and will use the graph database Neo4j for data storage, as well as implement common data operations such as query and update.

搜集汇总

背景与挑战

背景概述

该数据集是智能制造领域的多模态知识图谱资源，针对文本、图像、语音、视频等多模态内容，通过语义特征关联和Neo4j图数据库存储，旨在解决海量异构资源的管理与智能服务需求。它由中国科学院计算技术研究所发布，数据量为143.61MB，属于国家重点研发计划项目成果。