arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2204.04883cs.DLcs.AI
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科学资源与知识服务组件的精确画像

Accurate Portraits of Scientific Resources and Knowledge Service Components

Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia(智能电信软件与多媒体北京重点实验室);School of Computer Science (National Pilot School of Software Engineering)(计算机学院);Beijing University of Posts and Telecommunications(北京邮电大学)

Yue Wang, Zhe Xue, Ang Li

AI总结:

针对现有科技资源管理和分类标准的不足,提出结合知识图谱技术、文本表示学习、实体提取和知识服务组件构建科技资源精确画像的方案,以解决如何准确表示和挖掘科技资源潜在价值的问题。

摘要:

随着云计算时代到来,信息创建、获取和管理成本降低,网络数据呈爆炸式增长,科技资源不断上传。与新闻和社交媒体数据不同,科技资源主要由学术风格资源或实体构成,其关系网络丰富,能挖掘大量前沿科技信息。现有管理和分类标准难以涵盖所有实体与关联,无法准确提取重要信息。因此,如何从结构化和非结构化报告及文本构建完整准确的科技资源表示并挖掘其潜在价值成为紧迫问题。可行方案是结合知识图谱技术、文本表示学习、实体提取和知识服务组件构建科技资源的精确画像。

英文摘要:

With the advent of the cloud computing era, the cost of creating, capturing, and managing information has gradually decreased. The amount of data on the Internet is showing explosive growth, and more scientific and technological resources are being uploaded to the network. Different from news and social media data, scientific and technological resources are mainly composed of academic-style resources or entities, such as papers, patents, authors, and research institutions. There is a rich relationship network between these resources, from which a large amount of cutting-edge scientific and technological information can be mined. Existing scientific resource management and classification standards are difficult to completely cover all entities and associations, and they cannot accurately extract the important information contained in scientific and technological resources. Therefore, how to construct a complete and accurate representation of scientific and technological resources from structured and unstructured reports and texts, and how to tap the potential value of scientific and technological resources, are urgent problems. A feasible solution is to construct accurate portraits of scientific and technological resources by combining knowledge graph technology, text representation learning, entity extraction, and knowledge service components.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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