Abstract: Ontology validation is an important part of web
applications’ development, where knowledge integration and
ontological reasoning play a fundamental role. It aims to ensure the
consistency and correctness of ontological knowledge and to
guarantee that ontological reasoning is carried out in a meaningful
way. Existing approaches to ontology validation address more or less
specific validation issues, but the overall process of validating web
ontologies has not been formally established yet. As the size and the
number of web ontologies continue to grow, more web applications’
developers will rely on the existing repository of ontologies rather
than develop ontologies from scratch. If an application utilizes
multiple independently created ontologies, their consistency must be
validated and eventually adjusted to ensure proper interoperability
between them. This paper presents a validation technique intended to
test the consistency of independent ontologies utilized by a common
application.
Abstract: In the past few decades, the field of chemistry
education has grown tremendously and researches indicated that after
traditional chemistry instruction students often lacked deep
conceptual understanding and failed to integrate their ideas into
coherent conceptual framework. For several concepts in chemistry,
students at all levels have demonstrated difficulty in changing their
initial perceptions. Their perceptions are most often wrong and don't
agree with correct scientific concepts. This study explored the
effectiveness of intervention discussion sections for a college general
chemistry course designed to apply research on students
preconceptions, knowledge integration and student explanation.
Three interventions discussions lasting three hours on bond energy
and spontaneity were done tested and intervention (treatment)
students’ performances were compared with that of control group
which did not use the experimental pedagogy. Results indicated that
this instruction which was capable of identifying students'
misconceptions, initial conceptions and integrating those ideas into
class discussion led to enhanced conceptual understanding and better
achievement for the experimental group.
Abstract: In order to integrate knowledge in heterogeneous
case-based reasoning (CBR) systems, ontology-based CBR system
has become a hot topic. To solve the facing problems of
ontology-based CBR system, for example, its architecture is
nonstandard, reusing knowledge in legacy CBR is deficient, ontology
construction is difficult, etc, we propose a novel approach for
semi-automatically construct ontology-based CBR system whose
architecture is based on two-layer ontology. Domain knowledge
implied in legacy case bases can be mapped from relational database
schema and knowledge items to relevant OWL local ontology
automatically by a mapping algorithm with low time-complexity. By
concept clustering based on formal concept analysis, computing
concept equation measure and concept inclusion measure, some
suggestions about enriching or amending concept hierarchy of OWL
local ontologies are made automatically that can aid designers to
achieve semi-automatic construction of OWL domain ontology.
Validation of the approach is done by an application example.
Abstract: This study analyzed the creativity of student teams
participating in an exploratory information system development
project (ISDP) and examined antecedents of their creativity. By using
partial least squares (PLS) to analyze a sample of thirty-six teams
enrolled in an information system department project training course
that required three semesters of project-based lessons, the results
found social capitals (structural, relational and cognitive social capital)
positively influence knowledge integration. However, relational social
capital does not significantly influence knowledge integration.
Knowledge integration positively affects team creativity. This study
also demonstrated that social capitals significantly influence team
creativity through knowledge integration. The implications of our
findings for future research are discussed.
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.