Abstract
Instruments to predict the total costs associated with the development, deployment and ownership of ontology-based technologies are a must for their adoption by the industry. In previous work of ours we have introduced a series of models that analyzed and estimated the costs and benefits associated with the development of ontologies and related knowledge structures, and of the applications using them. This chapter can be seen as a continuation of this work as it provides guidelines – both scenario and tool-oriented – that assist project managers in utilizing these models throughout the ontology life cycle.
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Notes
- 1.
The survey did not cover reuse efforts related to the usage of ontologies in the context of the Linked Open Data (LOD) initiative, where a (relatively small) number of vocabularies is reused through so-called ’interlinking’. With LOD acting as a real game-changer in the semantic-technologies landscape, a new survey is required in order to fully understand the state-of-practice of ontology engineering in such data-driven scenarios.
- 2.
We use the cost-driver abbreviations defined in the ONTOCOM model [Popov et al. 2009]. A positive correlation means, that, if the value of one factor rises, the other rises as well; the opposite holds for negatively correlated factors. For instance, if OCAP decreases, the effort is expected to rise.
- 3.
FOLCOM calculates the time required to tag the entire collection.
- 4.
Dumb nodes are non-specialists which are information/data pushers. These roles may be incorporated into enterprise information systems.
- 5.
Active nodes are specialists that define the process dynamics and can only have an interface to enterprise knowledge portals.
- 6.
External nodes are specialists on a sub-process level. They should therefore be considered while designing enterprise information systems.
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The research leading to this paper was partially supported by the European Commission under the contract FP7-215040 “ACTIVE”.
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Bürger, T., Simperl, E., Wölger, S., Hangl, S. (2011). Using Cost-Benefit Information in Ontology Engineering Projects. In: Warren, P., Davies, J., Simperl, E. (eds) Context and Semantics for Knowledge Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19510-5_4
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