Sungyoung . Lee

Change management in evolving web ontologies

Knowledge constantly grows in scientific discourse and is revised over time by different stakeholders, either collaboratively or through institutionalized efforts. The body of knowledge gets structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of the issues. As a result, the knowledge model moves from a loosely clustered terminology to a semi-formal or even formal ontology. Change history management in such evolving knowledge models is an important and challenging task. Different techniques have been introduced in the research literature to solve the issue. A comprehensive solution must address various multi-faceted issues, such as ontology recovery, visualization of change effects, and keeping the evolving ontology in a consistent state. More so because the semantics of changes and evolution behavior of the ontology are hard to comprehend. This paper introduces a change history management framework for evolving ontologies; developed over the last couple of years. It is a comprehensive and methodological framework for managing issues related to change management in evolving ontologies, such as versioning, provenance, consistency, recovery, change representation and visualization. The Change history log is central to our framework and is supported by a semantically rich and formally sound change representation scheme known as change history ontology. Changes are captured and then stored in the log in conformance with the change history ontology. The log entries are later used to revert ontology to a previous consistent state, and to visualize the effects of change on ontology during its evolution. The framework is implemented to work as a plug-in for ontology repositories, such as Joseki and ontology editors, such as Protege. The change detection accuracy of the proposed system Change Tracer has been compared with that of Changes Tab, Version Log Generator in Protege; Change Detection, and Change Capturing of NeOn Toolkit. The proposed system has shown better accuracy against the existing systems. A comprehensive evaluation of the methodology was designed to validate the recovery operations. The accuracy of Roll-Back and Roll-Forward algorithms was conducted using different versions of SWETO Ontology, CIDOC CRM Ontology, OMV Ontology, and SWRC Ontology. Experimental results and comparison with other approaches shows that the change management process of the proposed system is accurate, consistent, and comprehensive in its coverage.

An Adaptive Semantic based Mediation System for Data Interoperability among Health Information Systems

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteRInteroperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

Towards Smart Homes Using Low Level Sensory Data

Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules.

Short Communication: Time efficient reconciliation of mappings in dynamic web ontologies

Mappings are established among ontologies for resolving the terminological and conceptual incompatibilities among information networks and information systems. Accommodating new knowledge in domain ontology causes the ontology to change from one consistent state to another. This consequently makes existing mappings among ontologies unreliable and stale due to the changes in resources. Mapping evolution eliminates discrepancies in the existing mappings. The proposed approach offers the benefits of re-establishing mappings among the updated ontologies in less time than is required with existing systems. It only considers the changed resources and eliminates staleness from the mappings. This approach uses the change history to drastically reduce the time required for reconciling mappings among ontologies, as shown in the results.

Mapping evolution of dynamic web ontologies

Information on the web and web services that are revised by stakeholders is growing incredibly. The presentation of this information has shifted from a representational model of web information with loosely clustered terminology to semi-formal terminology and even to formal ontology. Mediation (i.e., mapping) is required for systems and services to share information. Mappings are established between ontologies in order to resolve terminological and conceptual incompatibilities. Due to new discoveries in the field of information sharing, the body of knowledge has become more structured and refined. The domain ontologies that represent bodies of knowledge need to be able to accommodate new information. This allows for the ontology to evolve from one consistent state to another. Changes in resources cause existing mappings between ontologies to be unreliable and stale. This highlights the need for mapping evolution (regeneration) as it would eliminate the discrepancies from the existing mappings. In order to re-establish the mappings between dynamic ontologies, the existing systems require a complete mapping process to be restructured, and this process is time consuming. This paper proposes a mapping reconciliation approach between the updated ontologies that has been found to take less time to process compared to the time of existing systems when only the changed resources are considered and also eliminates the staleness of the existing mappings. The proposed approach employs the change history of ontology in order to store the ontology change information, which helps to drastically reduce the reconciliation time of the mappings between dynamic ontologies. A comprehensive evaluation of the performance of the proposed system on standard data sets has been conducted. The experimental results of the proposed system in comparison with six existing mapping systems are provided in this paper using 13 different data sets, which support our claims.

Context representation and fusion: advancements and opportunities.

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

Chord Based Identity Management for e-Healthcare Cloud Applications

Increase in healthcare awareness has raised the number of subscriptions for e-Healthcare applications. Consequently e-Healthcare application providers are adopting Cloud computing to handle immense processing load and to reduce service delivery cost. Identity management has been an issue which hinders in adoption of e-Healthcare applications, due to sensitivity of the data involved in it. Existing Cloud based e-Healthcare applications provide access to their services though Single-Sign-On (SSO) protocols. Traditional SSO uses asymmetric encryption, thus increasing the execution load on Cloud gateway and on Identity Provider as well. In this paper we propose a methodology of SSO for Cloud applications by utilizing Peer-to-Peer concepts to distribute processing load among computing nodes within Cloud. The proposed scheme, called Chord for Cloud (C4C), decreases the number of authentication request send to Identity Provider and disseminates the authentication process within the federated environment of Cloud, through Chord algorithm. The effectiveness of the proposed technique has been shown through argumentation scenarios.

A Mobile Sink Based Uneven Clustering Algorithm for Wireless Sensor Networks

Improving energy efficiency and prolonging network lifetime is a challenging research issue for wireless sensor networks (WSNs). Nowadays, adding mobility technology into WSNs has drawn increasing attention. In this paper, we combine the uneven clustering algorithm with mobile sink strategy and propose our mobile sink based uneven clustering algorithm. First, we study the uneven clustering algorithm with a fixed sink node located at the center of a rectangle network. We analyze the performance of energy consumption and network lifetime, and compare our algorithm with LEACH. Then we use mobile sink node instead of fixed sink node to collect fused data under similar environment. Simulation results show that mobile sink node can efficiently mitigate hot spots near sink node as sink node moves either randomly or along a predetermined fixed path.

Intelligent Manipulation of Human Activities Using Cloud Computing for u-Life Care

Ubiquitous Life Care (u-Life care) is one of the most focus area of research. To provide robust healthcare services, recognition of patient daily life activities is required. Context information with real-time daily life activities can help in better services, service suggestions, and change in system behaviour for better healthcare. Human health, profile, as well as activities are monitored and processed intelligently for better care with low cost. In this paper, we focus on intelligent manipulation of activities using Context-aware Activity Manipulation Engine (CAME) core of Human Activity Recognition Engine (HARE), recognized using video-based, wearable sensor-based and location-based activity recognition engines for context analysis of the activities performed. The objective of CAME is to receive real-time low level activity information from Activity Recognition Engines and infer higher level activities, make situation analysis, and after intelligent processing of activities with their corresponding information take appropriate decisions. To achieve this objective, two phase filtering technique for intelligent processing of information (represented in ontology) is used and appropriate decisions based on description logic rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively good accuracy.

After Effects of Ontology Evolution

Use of ontology in Information Systems and Knowledge Sharing Systems are increasing that gives more importance to proper maintenance of these ontologies in use. Ontology change management is a collaborative process that incorporate areas like; ontology engineering, evolution versioning, merging, integration, and maintenance. As experts develop better understanding of the domain, incorporate changes accordingly to the body of knowledge, as a result the body of knowledge evolves from one state to another. Preserving consistency while accommodating changes is a crucial task that needs special attention. In this paper we provide a brief review of state of the art in the field of ontology evolution that set the stage for the unfolded challenges in ontology evolution to complete the process automatically. Then we also discuss very important issues that need special attention to minimize the after effects of ontology evolution.