Visual data exploration for understanding and sharing of knowledge in large semantic datasets

Tracking #: 1630-2842

This paper is currently under review
Authors: 
Dmitry Mouromtsev1
Peter Haase
Dmitry Pavlov
Yury Emelyanov
Ariadna Barinova

Responsible editor: 
Guest Editors IE of Semantic Data 2017

Submission type: 
Full Paper
Abstract: 
The problem of understanding knowledge hidden in large datasets relates not only to information modelling and data identification but also to the processes of data exploration and consumption. Indeed, the major part of published data, especially Linked Data, has well-known schemas, metadata, and related descriptions. However, quite often the underlying knowledge of these datasets remain concealed for users. At the same time, it is well known that visual representation is the easiest and the most efficient way to dig into the meaning of data. Hence, the role of visual data exploration has become very important for understanding and re-using of knowledge. In this paper we introduce a visual step-by-step method and tool for dataset exploration through diagrammatic approach. We introduce the term Diagrammatic Question Answering to refer to the process of answering question addressed to a knowledge graph using visual means only. Also we present the exploratory system that uses the Wikidata as a knowledge graph, metaphactory as a knowledge graph platform and Ontodia library as visual tool for data interaction. We evaluate our approach through a user study and assessment of a diagramming process with experiment where two independent groups were involved. The first group worked with developed tool and created a number of diagram representing knowledge of Wikidata. And the second group tried to understand the meaning of these diagrams. The result showed the efficiency of diagrammatic approach for data exploration aimed at knowledge understanding and sharing.
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