Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies
IEEE Internet Computing
ONTOCOM Revisited: Towards Accurate Cost Predictions for Ontology Development Projects
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Netnography: Doing Ethnographic Research Online
Netnography: Doing Ethnographic Research Online
Adoption of Semantic Web from the perspective of technology innovation: A grounded theory approach
International Journal of Human-Computer Studies
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The decision of organizations to invest (or not) into a semantic application is, currently, often based on vague considerations and personal feelings. What is lacking is a model that would help determine whether semantic approaches would be adequate, given the aspects of the particular business and concrete adopter. Such a model would however need to take into account the heterogeneity of different applications that exhibit semantic features. We present a thorough exercise, and a prototypical methodology abstracted from it, for proceeding in multiple steps, from loosely sorted and purely textual descriptions of semantic applications to structured and instructive adopter readiness models. The whole process relies on expert-level manual analysis of textual descriptions, automatic cluster analysis (leading to plausible categories of semantic applications), critical factor analysis, questionnaire survey addressing the developers of applications, and adaptation of principles known from building multi-layer Capability Maturity Models. Although the overall approach relies to a large degree on (potentially subjective) manual analysis, very lightweight quantitative evaluation was also made for relevant steps in the process.