Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

Tracking #: 1981-3194

Marwan Al-Tawil1
Vania Dimitrova
Dhaval Thakker

Responsible editor: 
Krzysztof Janowicz

Submission type: 
Full Paper
This paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.
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Solicited Reviews:
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Review #1
By Valentina Maccatrozzo submitted on 06/Sep/2018
Review Comment:

I thank the authors for the improved manuscript. All the raised issues were properly addressed, so I recommend the manuscript for publication.

Review #2
By Bo Yan submitted on 07/Sep/2018
Review Comment:

The authors addressed my concerns and suggestions in the previous comments. The idea is original and the theories and experiment in the paper are solid. Therefore, I recommend this paper for publication.

Review #3
By Simon Scheider submitted on 14/Oct/2018
Minor Revision
Review Comment:

The authors did a very good and extensive job in addressing all the reviewer comments. They clarified their contribution, rewrote entire sections including the introduction to improve the motivational embedding with examples, cleared up significantly the methods sections including the argumentation for the metrics used, and added essential details to the description of the user study. Furthermore, the quality of the text and writing style improved a lot. The authors took much care in addressing all critique that I had.

There is only one issue that I still find problematic. This is the length of the article. It now counts 28 pages including references, and these pages are double column. I think it is necessary and possible to shorten the article. For example, the article now explains some methods twice, once in section 3 and in 5. Also section 7 and 8 are rather extensive and
could be summarized, maybe put part of the details into an appendix. The discussion in 9 is interesting but could be turned into a synopsis.

Otherwise I think the article is publishable.