Interleaving
Time and Structure
M.
Balaban
Abstract.
A model for knowledge representation that incorporates structure and time is
presented. The model is the domain of structured histories, which is based on
the interleaving of these features in a nested, structured manner. A rich
temporal representation language is provided. The interleaving structure and
time is shown to be fundamental to the representation of a variety of temporal
domains, including planning, knowledge bases, and design. Applications in the
areas of databases and music processing that are based on the structured
histories ontology are summarized.
The
novelty of the approach presented
in this paper is in the design of a general, domain independent, representation
scheme for capturing the aspects of structure and time. As a result, both
aspects can be declaratively specified in a single logical design, thereby
providing a basis for well founded processing of structured - temporal domains.
Information
Retrieval, Imaging and Probabilistic Logic
F. Sebastiani
Abstract.
Imaging is a class of non-Bayesian methods for the revision
of probability density functions originally proposed as a semantics for
conditional logic. Two of these revision functions, standard imaging and general
imaging, have successfully been applied to modelling information retrieval by
Crestani and van Rijsbergen. Due to
the problematic nature of a "direct" implementation of imaging
revision functions, in this paper we propose their alternative implementation by
representing the semantic structure that underlies imaging-based conditional
logics in the language of a probabilistic (Bayesian) logic. Besides showing the
potential of this "Bayesian" tool for the representation of
non-Bayesian revision functions, recasting these models of information retrieval
in such a general purpose knowledge representation and reasoning tool paves the
way to a possible integration of these models with other more KR-oriented
models, and to the exploitation of general-purpose domain-knowledge.
A
Relationship Between the Probabilistic and the Possibilistic Logic
Do
Van Thanh
Abstract. This paper points out that there exists a relationship between
the principle of Maximal Entropy (ME) in probability theory and the principle of
minimal Specificity (mS) in possibility theory via consistent transformations on
sets of probability and possibility distributions. By using these
transformations, the paper investigates the possibilities to apply methods of
probabilistic logic for possibility knowledge bases and conversely, reasoning
methods of possibilistic logic for probability knowledge bases.
Knowledge
Extraction by a Fuzzy Linguistic
Summary Approach
Ding-An
Chiang, Yi-Fan Wang, Wei Chen
Abstract. In some applications, it may be difficult to represent the
exact meaning of an object due to the nature of the environment being modelled.
Therefore, the object may only partially match certain properties, and crisp
data mining approaches may not be
appropriate for this situation. To overcome the situation, a linguistic summary
using the fuzzy set theory is developed as the data mining function in the KDD
system. Using predefined properties, the proposed summary can answer the
question "for each group in the database, which predefined property is
shared by largest number of objects?", where
the property in the question could fall in the range F1 Ú…Ú
Fm rather than a single property, Fi.
Some
Applications of Pedrycz's Operator
I.
Iancu
Abstract. In this paper we give a general form of the expression of
Pedrycz's operator and then we show
that this expression is useful to generate possibility and necessity measures
and also to represent the vague and uncertain facts under the form of other
vague and certain facts.