Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

Tracking #: 1779-2991

This paper is currently under review
Authors: 
Marwan Al-Tawil
Vania Dimitrova
Dhaval Thakker

Responsible editor: 
Krzysztof Janowicz

Submission type: 
Full Paper
Abstract: 
This paper investigates how to support a user’s exploration through a data graph in a way leading to expanding the user’s domain knowledge. To be effective, approaches to facilitate exploration of data graphs should take into account the utility from a user’s point of view. Our work focuses on knowledge utility – how useful exploration paths through a data graph are for expanding the user’s knowledge. We propose a new exploration support mechanism underpinned by the subsumption theory for meaningful learning, which postulates that new knowledge is grasped by starting from familiar entities in the data graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for adopting the subsumption theory for generating exploration paths is the automatic identification of knowledge anchors in a data graph (KADG). Several metrics for identifying KADG and the corresponding algorithms for implementation have been developed and evaluated against human cognitive structures. A subsumption algorithm which utilises KADG for generating exploration paths for knowledge expansion is presented and applied in the context of a data browser in a music domain. The resultant exploration paths are evaluated in a controlled user study to examine whether they increase the users’ knowledge as compared to free exploration. The findings show that exploration paths using knowledge anchors and subsumption lead to significantly higher increase in the users’ conceptual knowledge. The approach can be adopted in applications providing data graph exploration to facilitate learning and sensemaking of layman users who are not fully familiar with the domain presented in the data graph.
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