Intelligent information processing for building university knowledge base

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• 作者：Jakub Koperwas ; Łukasz Skonieczny…
• 关键词：Artificial intelligence ; Knowledge base ; Scientific resources ; Repository ; Digital library
• 刊名：Journal of Intelligent Information Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：48
• 期：1
• 页码：141-163
• 全文大小：3220KB
• 刊物类别：Computer Science
• 刊物主题：Information Storage and Retrieval; Data Structures, Cryptology and Information Theory; Artificial Intelligence (incl. Robotics); IT in Business; Document Preparation and Text Processing;
• 出版者：Springer US
• ISSN：1573-7675
• 卷排序：48

文摘

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than document-centric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.