Enterprise knowledge management: introducing new technologies in traditional Information Systems

Knowledge management systems described in research papers are rarely seen implemented in business realities, at least on a large scale. Companies are often tied to existing systems and cannot or would not revolutionize the situation to accommodate completely new solutions. Given this assumption, this work investigates several small-scale modifications that could be applied to in-place Information Systems so as to improve them with new technologies without major transformations and service discontinuities. The focus is interoperability, with a particular stress on the promotion of the ebXML registry standard. A universal interface for document management was defined, and the conforming “interoperable” DMSs were arranged within an architecture explicitly designed for ebXML-compliant access. This allowed standards-based manipulation of legacy DM systems. The closely related topic of Semantic knowledge management was also tackled. We developed Semantic tools integration for traditional repositories with low architectural impact. Finally, we discussed a novel issue in document categorization, and a new kind of ontology that could be used in that contex

Lifecycle-Support in Architectures for Ontology-Based Information Systems

Ontology-based applications play an increasingly important role in the public and corporate Semantic Web. While today there exist a range of tools and technologies to support specific ontology engineering and management activities, architectural design guidelines for building ontology-based applications are missing. In this paper, we present an architecture for ontology-based applications—covering the complete ontology-lifecycle—that is intended to support software engineers in designing and developing ontology based-applications. We illustrate the use of the architecture in a concrete case study using the NeOn toolkit as one implementation of the architecture

Orta Doğu Teknik Üniversitesi- Bilgi Toplumu Teknolojileri- Mükemmeliyet merkez projesi

TÜBİTAK EEEAG Proje01.02.200

Correctness-aware high-level functional matching approaches for semantic web services

Existing service matching approaches trade precision for recall, creating the need for humans to choose the correct services, which is a major obstacle for automating the service matching and the service aggregation processes. To overcome this problem, the matchmaker must automatically determine the correctness of the matching results according to the defined users' goals. That is, only service(s)-achieving users' goals are considered correct. This requires the high-level functional semantics of services, users, and application domains to be captured in a machine-understandable format. Also this requires the matchmaker to determine the achievement of users' goals without invoking the services. We propose the G+ model to capture the high-level functional specifications of services and users (namely goals, achievement contexts and external behaviors) providing the basis for automated goal achievement determination; also we propose the concepts substitutability graph to capture the application domains' semantics. To avoid the false negatives resulting from adopting existing constraint and behavior matching approaches during service matching, we also propose new constraint and behavior matching approaches to match constraints with different scopes, and behavior models with different number of state transitions. Finally, we propose two correctness-aware matching approaches (direct and aggregate) that semantically match and aggregate semantic web services according to their G+ models, providing the required theoretical proofs and the corresponding verifying simulation experiments

E-Government Interoperability and Integration Architecture Modeling Using TOGAF Framework Based on Service Oriented Architecture

. Development of e-Government in Indonesia continues to roll and run in every government agency, both in central government and local government. Implementation of e-Government means that there are government's efforts to build and improve the quality of public services and internal operations in each regional apparatus organization effectively and efficiently. These activities inline with one of the bureaucracy reform mission, that is the modernization of government bureaucracy through the using of information and communication technology optimization to support bureaucracy reform vision and the creation of good governance. The development and implementation of e-Government currently mostly implemented by each regional apparatus organization. This condition makes it difficult to exchange data and information related to multisectoral activities due to data and information spread across various databases from different applications, platform, environment and architecture without good documentation. In preparation for arranging the e-Government architecture, we will use The Open Group Architecture Framework - Architecture Development Method as an enterprise architecture framework. The purpose of this research is to study and conceptualize e-Government interoperability and integration solutions of all existing applications and providing some model documents such as e-Government Architecture Vision, Integrated Business Model References Architecture, and Integrated Data Model References Architecture

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF