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Context dependent semantic granularity
Author(s) -
Riccardo Albertoni,
Elena Camossi,
Mónica De Martino,
Franca Giannini,
Marina Monti
Publication year - 2011
Publication title -
international journal of data mining modelling and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 14
eISSN - 1759-1163
pISSN - 1759-1171
DOI - 10.1504/ijdmmm.2011.041495
Subject(s) - computer science , granularity , abstraction , ontology , flexibility (engineering) , context (archaeology) , information retrieval , semantics (computer science) , space (punctuation) , data mining , human–computer interaction , data science , programming language , paleontology , philosophy , statistics , mathematics , epistemology , biology , operating system
A fundamental issue to improve the accessibility to information resources is how to efficiently deal with huge amount(s) of data. In this respect, ontology driven techniques are expected to improve the overlap between the Cognitive Space applied by the user and the Information Space, which is defined by the information providers. In this paper we describe a powerful method to extract semantic granularities, which enable the navigation of a repository according to different levels of abstraction. In the formalisation we present, granularities are explicitly parameterised according to criteria induced by the context, which improves the method flexibility. Furthermore, the parameterisation assists the user allowing to formulate and refine the browsing criteria. Case studies are described to demonstrate how granularities ease the information sources browsing and to illustrate how they may vary according to the context. A validation of the cognitive principles behind the method is presented, together with the analysis of the results obtained by the experimentation.

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