Abstract: Relational databases are often used as a basis for persistent storage of ontologies to facilitate rapid operations such as search and retrieval, and to utilize the benefits of relational databases management systems such as transaction management, security and integrity control. On the other hand, there appear more and more OWL files that contain ontologies. Therefore, this paper proposes to extract ontologies from OWL files and then store them in relational databases. A prerequisite for this storing is transformation of ontologies to relational databases, which is the purpose of this paper.
Abstract: As more people from non-technical backgrounds
are becoming directly involved with large-scale ontology
development, the focal point of ontology research has shifted
from the more theoretical ontology issues to problems
associated with the actual use of ontologies in real-world,
large-scale collaborative applications. Recently the National
Science Foundation funded a large collaborative ontology
development project for which a new formal ontology model,
the Ontology Abstract Machine (OAM), was developed to
satisfy some unique functional and data representation
requirements. This paper introduces the OAM model and the
related algorithms that enable maintenance of an ontology that
supports node-based user access. The successful software
implementation of the OAM model and its subsequent
acceptance by a large research community proves its validity
and its real-world application value.
Abstract: Ontologies are broadly used in the context of networked home environments. With ontologies it is possible to define and store context information, as well as to model different kinds of physical environments. Ontologies are central to networked home environments as they carry the meaning. However, ontologies and the OWL language is complex. Several ontology visualization approaches have been developed to enhance the understanding of ontologies. The domain of networked home environments sets some special requirements for the ontology visualization approach. The visualization tool presented here, visualizes ontologies in a domain-specific way. It represents effectively the physical structures and spatial relationships of networked home environments. In addition, it provides extensive interaction possibilities for editing and manipulating the visualization. The tool shortens the gap from beginner to intermediate OWL ontology reader by visualizing instances in their actual locations and making OWL ontologies more interesting and concrete, and above all easier to comprehend.
Abstract: This paper focuses on a novel method for semantic
searching and retrieval of information about learning materials.
Metametadata encapsulate metadata instances by using the properties
and attributes provided by ontologies rather than describing learning
objects. A novel metametadata taxonomy has been developed which
provides the basis for a semantic search engine to extract, match and
map queries to retrieve relevant results. The use of ontological views
is a foundation for viewing the pedagogical content of metadata
extracted from learning objects by using the pedagogical attributes
from the metametadata taxonomy. Using the ontological approach
and metametadata (based on the metametadata taxonomy) we present
a novel semantic searching mechanism.These three strands – the
taxonomy, the ontological views, and the search algorithm – are
incorporated into a novel architecture (OMESCOD) which has been
implemented.
Abstract: The internet has become an attractive avenue for
global e-business, e-learning, knowledge sharing, etc. Due to
continuous increase in the volume of web content, it is not practically
possible for a user to extract information by browsing and integrating
data from a huge amount of web sources retrieved by the existing
search engines. The semantic web technology enables advancement
in information extraction by providing a suite of tools to integrate
data from different sources. To take full advantage of semantic web,
it is necessary to annotate existing web pages into semantic web
pages. This research develops a tool, named OWIE (Ontology-based
Web Information Extraction), for semantic web annotation using
domain specific ontologies. The tool automatically extracts
information from html pages with the help of pre-defined ontologies
and gives them semantic representation. Two case studies have been
conducted to analyze the accuracy of OWIE.
Abstract: Multi-agent communication of Semantic Web
information cannot be realized without the need to reason with
ontology and agent locations. This is because for an agent to be able to
reason with an external semantic web ontology, it must know where
and how to access to that ontology. Similarly, for an agent to be able to
communicate with another agent, it must know where and how to send
a message to that agent. In this paper we propose a framework of an
agent which can reason with ontology and agent locations in order to
perform reasoning with multiple distributed ontologies and perform
communication with other agents on the semantic web. The agent
framework and its communication mechanism are formulated entirely
in meta-logic.
Abstract: Meta-reasoning is essential for multi-agent communication. In this paper we propose a framework of multi-agent communication in which agents employ meta-reasoning to reason with agent and ontology locations in order to communicate semantic information with other agents on the semantic web and also reason with multiple distributed ontologies. We shall argue that multi-agent communication of Semantic Web information cannot be realized without the need to reason with agent and ontology locations. This is because for an agent to be able to communicate with another agent, it must know where and how to send a message to that agent. Similarly, for an agent to be able to reason with an external semantic web ontology, it must know where and how to access to that ontology. The agent framework and its communication mechanism are formulated entirely in meta-logic.
Abstract: e-Government is already in its second decade. Prerequisite for further development and adaptation to new realities is the optimal management of administrative information and knowledge production by those involved, i.e. the public sector, citizens and businesses. Nowadays, the amount of information displayed or distributed on the Internet has reached enormous dimensions, resulting in serious difficulties when extracting and managing knowledge. The semantic web is expected to play an important role in solving this problem and the technologies that support it. In this article, we address some relevant issues.
Abstract: This paper discusses the designing of knowledge
integration of clinical information extracted from distributed medical
ontologies in order to ameliorate a machine learning-based multilabel
coding assignment system. The proposed approach is
implemented using a decision tree technique of the machine learning
on the university hospital data for patients with Coronary Heart
Disease (CHD). The preliminary results obtained show a satisfactory
finding that the use of medical ontologies improves the overall
system performance.
Abstract: Information sharing and exchange, rather than
information processing, is what characterizes information
technology in the 21st century. Ontologies, as shared common
understanding, gain increasing attention, as they appear as the
most promising solution to enable information sharing both at
a semantic level and in a machine-processable way. Domain
Ontology-based modeling has been exploited to provide
shareability and information exchange among diversified,
heterogeneous applications of enterprises.
Contextual ontologies are “an explicit specification of
contextual conceptualization". That is: ontology is
characterized by concepts that have multiple representations
and they may exist in several contexts. Hence, contextual
ontologies are a set of concepts and relationships, which are
seen from different perspectives. Contextualization is to allow
for ontologies to be partitioned according to their contexts.
The need for contextual ontologies in enterprise modeling
has become crucial due to the nature of today's competitive
market. Information resources in enterprise is distributed and
diversified and is in need to be shared and communicated
locally through the intranet and globally though the internet.
This paper discusses the roles that ontologies play in an
enterprise modeling, and how ontologies assist in building a
conceptual model in order to provide communicative and
interoperable information systems. The issue of enterprise
modeling based on contextual domain ontology is also
investigated, and a framework is proposed for an enterprise
model that consists of various applications.
Abstract: Hybrid knowledge model is suggested as an underlying
framework for product development management. It can support such
hybrid features as ontologies and rules. Effective collaboration in
product development environment depends on sharing and reasoning
product information as well as engineering knowledge. Many studies
have considered product information and engineering knowledge.
However, most previous research has focused either on building the
ontology of product information or rule-based systems of engineering
knowledge. This paper shows that F-logic based knowledge model can
support such desirable features in a hybrid way.
Abstract: One of object oriented software developing problem
is the difficulty of searching the appropriate and suitable objects for
starting the system. In this work, ontologies appear in the part of
supporting the object discovering in the initial of object oriented
software developing. There are many researches try to demonstrate
that there is a great potential between object model and ontologies.
Constructing ontology from object model is called ontology
engineering can be done; On the other hand, this research is aiming to
support the idea of building object model from ontology is also
promising and practical. Ontology classes are available online in any
specific areas, which can be searched by semantic search engine.
There are also many helping tools to do so; one of them which are
used in this research is Protégé ontology editor and Visual Paradigm.
To put them together give a great outcome. This research will be
shown how it works efficiently with the real case study by using
ontology classes in travel/tourism domain area. It needs to combine
classes, properties, and relationships from more than two ontologies
in order to generate the object model. In this paper presents a simple
methodology framework which explains the process of discovering
objects. The results show that this framework has great value while
there is possible for expansion. Reusing of existing ontologies offers
a much cheaper alternative than building new ones from scratch.
More ontologies are becoming available on the web, and online
ontologies libraries for storing and indexing ontologies are increasing
in number and demand. Semantic and Ontologies search engines have
also started to appear, to facilitate search and retrieval of online
ontologies.
Abstract: Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.
Abstract: The number of framework conceived for e-learning
constantly increase, unfortunately the creators of learning materials
and educational institutions engaged in e-formation adopt a
“proprietor" approach, where the developed products (courses,
activities, exercises, etc.) can be exploited only in the framework
where they were conceived, their uses in the other learning
environments requires a greedy adaptation in terms of time and
effort. Each one proposes courses whose organization, contents,
modes of interaction and presentations are unique for all learners,
unfortunately the latter are heterogeneous and are not interested by
the same information, but only by services or documents adapted to
their needs. Currently the new tendency for the framework
conceived for e-learning, is the interoperability of learning materials,
several standards exist (DCMI (Dublin Core Metadata Initiative)[2],
LOM (Learning Objects Meta data)[1], SCORM (Shareable Content
Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote
Instructional Authoring and Distribution Networks for Europe)[9],
CANCORE (Canadian Core Learning Resource Metadata
Application Profiles)[3]), they converge all to the idea of learning
objects. They are also interested in the adaptation of the learning
materials according to the learners- profile. This article proposes an
approach for the composition of courses adapted to the various
profiles (knowledge, preferences, objectives) of learners, based on
two ontologies (domain to teach and educational) and the learning
objects.
Abstract: Business process model describes process flow of a
business and can be seen as the requirement for developing a
software application. This paper discusses a BPM2CD guideline
which complements the Model Driven Architecture concept by
suggesting how to create a platform-independent software model in
the form of a UML class diagram from a business process model. An
important step is the identification of UML classes from the business
process model. A technique for object-oriented analysis called
domain analysis is borrowed and key concepts in the business
process model will be discovered and proposed as candidate classes
for the class diagram. The paper enhances this step by using ontology
search to help identify important classes for the business domain. As
ontology is a source of knowledge for a particular domain which
itself can link to ontologies of related domains, the search can give a
refined set of candidate classes for the resulting class diagram.