Abstract
Ontology change log data is a valuable source of information which reflects the changes in the domain, the user requirements, flaws in the initial design or the need to incorporate additional information. Ontology change logs can provide operational as well as analytical support in the ontology evolution process. In this paper, we present a novel approach to deal with change representation and knowledge discovery from ontology change logs. We look into different knowledge gathering aspects to capture every single facet of ontology change. The ontology changes are formalised using a graph-based approach. The knowledge-based change log facilitates detection of similarities within different time series, discovering implicit dependencies between ontological entities and reuse of knowledge. We analyse an ontology change log graph in order to identify frequent changes that occur in ontologies over time. We identify different types of change sequences based on their order and completeness. Analysis of change logs also assists in extracting new change patterns and rules which cannot be found by simply querying or processing ontology change logs.
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Javed, M., Abgaz, Y.M., Pahl, C. (2011). Towards Implicit Knowledge Discovery from Ontology Change Log Data. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_13
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DOI: https://doi.org/10.1007/978-3-642-25975-3_13
Publisher Name: Springer, Berlin, Heidelberg
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