Towards imaging large-scale ontologies for quick understanding and analysis

  • Authors:

  • KeWei Tu;Miao Xiong;Lei Zhang;HaiPing Zhu;Jie Zhang;Yong Yu

  • Affiliations:

  • Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China

  • Venue:
  • ISWC'05 Proceedings of the 4th international conference on The Semantic Web
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

In many practical applications, ontologies tend to be very large and complicated. In order for users to quickly understand and analyze large-scale ontologies, in this paper we propose a novel ontology visualization approach, which aims to complement existing approaches like the hierarchy graph. Specifically, our approach produces a holistic “imaging” of the ontology which contains a semantic layout of the ontology classes. In addition, the distributions of the ontology instances and instance relations are also depicted in the “imaging”. We introduce at length the key techniques and algorithms used in our approach. Then we examine the resulting user interface and find it facilitates tasks like ontology navigation, ontology retrieval and ontology instance analysis.