Abstract: A knowledge base stores facts and rules about the
world that applications can use for the purpose of reasoning. By
applying the concept of granular computing to a knowledge base,
several advantages emerge. These can be harnessed by applications
to improve their capabilities and performance. In this paper, the
concept behind such a construct, called a granular knowledge cube,
is defined, and its intended use as an instrument that manages to
cope with different data types and detect knowledge domains is
elaborated. Furthermore, the underlying architecture, consisting of the
three layers of the storing, representing, and structuring of knowledge,
is described. Finally, benefits as well as challenges of deploying it
are listed alongside application types that could profit from having
such an enhanced knowledge base.
Abstract: An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.
Abstract: Knowledge bases are basic components of expert
systems or intelligent computational programs. Knowledge bases
provide knowledge, events that serve deduction activity,
computation and control. Therefore, researching and developing of
models for knowledge representation play an important role in
computer science, especially in Artificial Intelligence Science and
intelligent educational software. In this paper, the extensive
deduction computational model is proposed to design knowledge
bases whose attributes are able to be real values or functional values.
The system can also solve problems based on knowledge bases.
Moreover, the models and algorithms are applied to produce the
educational software for solving alternating current problems or
solving set of equations automatically.
Abstract: We introduce a logic-based framework for database
updating under constraints. In our framework, the constraints are
represented as an instantiated extended logic program. When performing
an update, database consistency may be violated. We provide
an approach of maintaining database consistency, and study the
conditions under which the maintenance process is deterministic. We
show that the complexity of the computations and decision problems
presented in our framework is in each case polynomial time.
Abstract: The paper represents a reflection on how to select proper indicators to assess the progress of regional contexts towards a knowledge-based society. Taking the first research methodologies elaborated at an international level (World Bank, OECD, etc.) as a reference point, this work intends to identify a set of indicators of the knowledge economy suitable to adequately understand in which manner and to which extent the territorial development dynamics are correlated with the knowledge-base of the considered local society. After a critical survey of the variables utilized within other approaches adopted by international or national organizations, this paper seeks to elaborate a framework of variables, named Regional Knowledge Economy Indicators (ReKEI), necessary to describe the knowledge-based relations of subnational socio-economic contexts. The realization of this framework has a double purpose: an analytical one consisting in highlighting the regional differences in the governance of knowledge based processes, and an operative one consisting in providing some reference parameters for contributing to increasing the effectiveness of those economic policies aiming at enlarging the knowledge bases of local societies.
Abstract: This paper proposes a declarative language for
knowledge representation (Ibn Rochd), and its environment of
exploitation (DeGSE). This DeGSE system was designed and
developed to facilitate Ibn Rochd writing applications. The system
was tested on several knowledge bases by ascending complexity,
culminating in a system for recognition of a plant or a tree, and
advisors to purchase a car, for pedagogical and academic guidance,
or for bank savings and credit. Finally, the limits of the language and
research perspectives are stated.
Abstract: In this paper, we focus on the use of knowledge bases
in two different application areas – control of systems with unknown
or strongly nonlinear models (i.e. hardly controllable by the classical
methods), and robot motion planning in eight directions. The first
one deals with fuzzy logic and the paper presents approaches for
setting and aggregating the rules of a knowledge base. Te second one
is concentrated on a case-based reasoning strategy for finding the
path in a planar scene with obstacles.
Abstract: The data exchanged on the Web are of different nature
from those treated by the classical database management systems;
these data are called semi-structured data since they do not have a
regular and static structure like data found in a relational database;
their schema is dynamic and may contain missing data or types.
Therefore, the needs for developing further techniques and
algorithms to exploit and integrate such data, and extract relevant
information for the user have been raised. In this paper we present
the system OSIX (Osiris based System for Integration of XML
Sources). This system has a Data Warehouse model designed for the
integration of semi-structured data and more precisely for the
integration of XML documents. The architecture of OSIX relies on
the Osiris system, a DL-based model designed for the representation
and management of databases and knowledge bases. Osiris is a viewbased
data model whose indexing system supports semantic query
optimization. We show that the problem of query processing on a
XML source is optimized by the indexing approach proposed by
Osiris.