Modelling Scholarly Debate: Conceptual Foundations for Knowledge Domain Analysis Technology

Benn, Neil Jefferson Lavere (2009). Modelling Scholarly Debate: Conceptual Foundations for Knowledge Domain Analysis Technology. PhD thesis The Open University.



Knowledge Domain Analysis (KDA) research investigates computational support for users who desire to understand and/or participate in the scholarly inquiry of a given academic knowledge domain. KDA technology supports this task by allowing users to identify important features of the knowledge domain such as the predominant research topics, the experts in the domain, and the most influential researchers. This thesis develops the conceptual foundations to integrate two identifiable strands of KDA research: Library and Information Science (LIS), which commits to a citation-based Bibliometrics paradigm, and Knowledge Engineering (KE), which adopts an ontology-based Conceptual Modelling paradigm. A key limitation of work to date is its inability to provide machine-readable models of the debate in academic knowledge domains. This thesis argues that KDA tools should support users in understanding the features of scholarly debate as a prerequisite for engaging with their chosen domain.

To this end, the thesis proposes a Scholarly Debate Ontology which specifies the formal vocabulary for constructing representations of debate in academic knowledge domains. The thesis also proposes an analytical approach that is used to automatically detect clusters of viewpoints as particularly important features of scholarly debate. This approach combines aspects of both the Conceptual Modelling and Bibliometrics paradigms. That is, the method combines an ontological focus on semantics and a graph-theoretical focus on structure in order to identify and reveal new insights about viewpoint-clusters in a given knowledge domain. This combined ontological and graph-theoretical approach is demonstrated and evaluated by modelling and analysing debates in two domains. The thesis reflects on the strengths and limitations of this approach, and considers the directions which this work opens up for future research into KDA technology.

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