7,738 research outputs found
On similarity in fuzzy description logics
This paper is a contribution to the study of similarity relations between objects represented as attribute-value pairs in Fuzzy Description Logics . For this purpose we use concrete domains in the fuzzy description logic IALCEF(D)IALCEF(D) associated either with a left-continuous or with a finite t-norm. We propose to expand this fuzzy description logic by adding a Similarity Box (SBox) including axioms expressing properties of fuzzy equalities. We also define a global similarity between objects from similarities between the values of each object attribute (local similarities) and we prove that the global similarity defined using a t-norm inherits the usual properties of the local similarities (reflexivity, symmetry or transitivity). We also prove a result relative to global similarities expressing that, in the context of the logic MTL∀, similar objects have similar properties, being these properties expressed by predicate formulas evaluated in these object
On the similarity relation within fuzzy ontology components
Ontology reuse is an important research issue. Ontology
merging, integration, mapping, alignment and versioning
are some of its subprocesses. A considerable research work has
been conducted on them. One common issue to these subprocesses
is the problem of defining similarity relations among ontologies
components. Crisp ontologies become less suitable in all domains
in which the concepts to be represented have vague, uncertain
and imprecise definitions. Fuzzy ontologies are developed to
cope with these aspects. They are equally concerned with the
problem of ontology reuse. Defining similarity relations within
fuzzy context may be realized basing on the linguistic similarity
among ontologies components or may be deduced from their
intentional definitions. The latter approach needs to be dealt
with differently in crisp and fuzzy ontologies. This is the scope
of this paper.ou
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Towards a Proof Theory of G\"odel Modal Logics
Analytic proof calculi are introduced for box and diamond fragments of basic
modal fuzzy logics that combine the Kripke semantics of modal logic K with the
many-valued semantics of G\"odel logic. The calculi are used to establish
completeness and complexity results for these fragments
Probabilistic Dynamic Logic of Phenomena and Cognition
The purpose of this paper is to develop further the main concepts of
Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in
the previous paper. The specific character of these logics is in matching
vagueness or fuzziness of similarity measures to the uncertainty of models.
These logics are based on the following fundamental notions: generality
relation, uncertainty relation, simplicity relation, similarity maximization
problem with empirical content and enhancement (learning) operator. We develop
these notions in terms of logic and probability and developed a Probabilistic
Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of
probabilistic models of brain. In our research the effectiveness of suggested
formalization is demonstrated by approximation of the expert model of breast
cancer diagnostic decisions. The P-DL-PC logic was previously successfully
applied to solving many practical tasks and also for modelling of some
cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence
July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number:
CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336
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