By Paul Warren, John Davies, Elena Simperl
Knowledge and data are one of the greatest resources of companies and firms. although, successfully dealing with, keeping, gaining access to, and reusing this intangible treasure is hard. info overload makes it tricky to target the data that truly concerns; the truth that a lot company wisdom in simple terms is living in staff’ heads heavily hampers reuse.
The paintings defined during this e-book is inspired via the necessity to raise the productiveness of information paintings. according to effects from the EU-funded lively undertaking and complemented by way of contemporary similar effects from different researchers, the applying of 3 methods is gifted: the synergy of internet 2.0 and semantic know-how; context-based details supply; and using expertise to aid casual person strategies. The contributions are equipped in 5 elements. half I contains a common creation and an outline of the possibilities and demanding situations confronted via businesses in exploiting net 2.0 functions. half II seems on the applied sciences, and likewise a few methodologies, built in lively. half III describes how those applied sciences were evaluated in 3 case reviews in the venture. half IV begins with a bankruptcy describing the primary marketplace tendencies for wisdom administration recommendations, after which contains a variety of chapters describing paintings complementary to lively. ultimately, half V attracts conclusions and shows additional parts for research.
Overall, this publication in general goals at researchers in academia and searching for a state of the art review of using semantic and net 2.0 applied sciences for wisdom administration and private productiveness. Practitioners in also will gain, particularly from the case experiences which spotlight state of the art purposes in those fields.
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Additional resources for Context and Semantics for Knowledge Management: Technologies for Personal Productivity
In addition, formal, logics-based representation languages require specific expertise within the ontology development and maintenance team. benefits involved and their analysis (see Chapter ‘Using Cost-Benefit Information in Ontology Engineering Projects’). 3 How are Enterprise Knowledge Structures Being Built In this section, we first give a short overview of the wiki technology Semantic MediaWiki (SMW) (Kr€ otzsch et al. 2007) as a flexible tool for dealing with enterprise knowledge structures.
0 and Network Intelligence 19 recognition algorithms and Amazon’s recommendation systems are based on data about user actions and behaviours. Google’s spell checker uses an analysis of usergenerated corrections of mis-spelt queries to hone the appropriateness of its responses and to generate the “Did you mean. ” service for its query recognition, whilst its page-ranking algorithm presents the most “valuable” items first, based on its analysis of the way in which different kinds of users access and spend time on the various web-pages.
Domain ontologies: they are used to model specific domains in medicine or academia. A typical example in this area is the Gene Ontology developed by the Gene Ontology Consortium (2000). – Task ontologies: they describe general or domain-specific activities. – Application ontologies: they are extensions of domain ontologies having regard to particular application-related task ontologies and application requirements. A last category of ontologies, which was not covered by the classifications mentioned so far, are the so-called meta-ontologies or (knowledge) representation ontologies.