99,717 research outputs found
Ontology-Based Semantic Retrieval for Education Management Systems
The traditional information retrieval technologies are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. As a new technology of information retrieval, semantic retrieval can retrieve information resource fully and precisely based on the knowledge understanding and knowledge reasoning. Ontology, which can well represent and reason about the domain knowledge, is proved to be very useful in the semantic retrieval. On this basis, in this paper, we propose a complete ontology-based semantic retrieval approach and framework for education management system. Firstly, we present some rules for constructing domain ontology from the education management system; Then, a semantic annotation method of the constructed ontology is given; Further, the ontologybased semantic retrieval algorithmis proposed; Finally, a complete framework is developed and some experiments are done. Conducted experiments show that our semantic retrieval model obtained comparable and better performance results than the traditional information retrieval technology for education management system
AKTing for the Consumer
Knowledge management is all about delivering data to the right person at the right time and in the right format. Yet there are massive amounts of very valuable data stored in inaccessible databases, and written in machine unreadable formats. The applications below attempt to unlock the potential of some of this data, and serve it back to the consumer. Funded by the Office of Public Sector Information (OPSI) to demonstrate how Semantic Web technology can be used by government to unlock the potential of public sector information
SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology
In this era of knowledge economy in which knowledge have become the most precious
resource, surveys have shown that e-Learning has been on the increasing trend in various
organizations including, among others, education and corporate. The use of e-Learning is
not only aim to acquire knowledge but also to maintain competitiveness and advantages
for individuals or organizations. However, the early promise of e-Learning has yet to be
fully realized, as it has been no more than a handout being published online, coupled with
simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by
Web 2.0 technology still hardly overcome common problem such as information
overload and poor content aggregation in a highly increasing number of learning objects
in an e-Learning Management System (LMS) environment.
The aim of this research study is to exploit the Semantic Web (SW) and Knowledge
Management (KM) technology; the two emerging and promising technology to enhance
the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge
Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is
the backbone of SW and KM is introduced for managing knowledge especially from
learning object and developing automated question answering system (Aquas) with
expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to
develop the Ontology in this research work.
The potential of SW and KM technology is identified in this research finding which will
benefit e-Learning developer to develop e-Learning system especially with social
constructivist pedagogical approach from the point of view of KM framework and SW
environment. The (semi-) automatic ontological knowledge base construction system
(SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically
whilst the Aquas with expert locator has facilitated knowledge retrieval
that encourages knowledge sharing in e-Learning environment.
The experiment conducted has shown that the SAOKBCS can extract concept that is the
main component of Ontology from text learning object with precision of 86.67%, thus
saving the expert time and effort to build Ontology manually. Additionally the
experiment on Aquas has shown that more than 80% of users are satisfied with answers
provided by the system. The expert locator framework can also improve the performance
of Aquas in the future usage.
Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS),
semi-automatic ontological knowledge base construction system (SAOKBCS),
automated question answering system (Aquas), Ontology, expert locator
Integration and verification of semantic constraints in adaptive process management systems
Adaptivity in process management systems is key to their successful applicability in practice. Approaches have been
already developed to ensure system correctness after arbitrary process changes at the syntactical level (e.g., avoiding inconsistencies such as deadlocks or missing input parameters after a process change). However, errors may be still caused at the semantical level (e.g., violation of business rules). Therefore, the integration and verification of domain knowledge will flag a milestone in the development of adaptive process management technology. In this paper, we introduce a framework for defining semantic constraints over processes in such a way that they can express real-world domain knowledge on the one hand and are still manageable concerning the effort for maintenance and semantic process verification on the other hand. This can be used to detect semantic conflicts (e.g., drug incompatibilities) when modeling process templates, applying ad hoc changes at process instance level, and propagating process template modifications to already running process instances, even if they have been already individually modified themselves; i.e., we present techniques to ensure semantic correctness for single and concurrent changes which are, in addition, minimal regarding the set of semantic constraints to be checked. Together with further optimizations of the semantic checks based on certain process meta model properties this allows for efficiently verifying processes. Altogether, the framework presented in this paper provides the basis for process management systems which are adaptive and semantic-aware at the same time
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