Herding Linked Data: Semantic Search and Navigation Among Scholarly Datasets

作     者：Koutsomitropoulos, Dimitrios A. Solomou, Georgia D. Kalou, Aikaterini K. 

作者机构：Univ Patras Comp Engn & Informat Dept High Performance Informat Syst Lab HPCLab Patras 26500 Greece 

出 版 物：《INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING》 (Int. J. Semantic Computing)

年 卷 期：2015年第9卷第4期

页      面：459-482页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：UK Open University National Research Council, NRC College of Natural Resources, University of California Berkeley, CNR 

主　　题：LOD ontologies entity extraction reasoning learning object repositories 

摘      要：Linked Data seem to play a seminal role in the establishment of the Semantic Web as the next-generation Web. This is even more important for digital object collections and educational institutions that aim not only at promoting and disseminating their content but also at aiding its discoverability and contextualization. In this paper we show how repository metadata can be exposed as Linked Data, thus enhancing their machine understandability and contributing to the LOD cloud. We use a popular digital repository system, namely DSpace, as our deployment platform. Without requiring additional annotations that would harden the curation task, educational resources are semantically enhanced by reusing and transforming existing metadata values. Our effort comes complete with an updated UI that allows for reasoning-based search and navigation between linked resources within and outside the scope of the digital repository. Therefore ontological descriptions of resources can now be accessed from within the repository s core context, linked from outside datasets, link to external datasets and get discovered by semantic search.