Semantically intelligent semi-automated ontology integration

An ontology is a way of information categorization and storage. Web Ontologies provide help in retrieving the required and precise information over the web. However, the problem of heterogeneity between ontologies may occur in the use of multiple ontologies of the same domain. The integration of ontologies provides a solution for the heterogeneity problem. Ontology integration is a solution to problem of interoperability in the knowledge based systems. Ontology integration provides a mechanism to find the semantic association between a pair of reference ontologies based on their concepts. Many researchers have been working on the problem of ontology integration; however, multiple issues related to ontology integration are still not addressed. This dissertation involves the investigation of the ontology integration problem and proposes a layer based enhanced framework as a solution to the problem. The comparison between concepts of reference ontologies is based on their semantics along with their syntax in the concept matching process of ontology integration. The semantic relationship of a concept with other concepts between ontologies and the provision of user confirmation (only for the problematic cases) are also taken into account in this process. The proposed framework is implemented and validated by providing a comparison of the proposed concept matching technique with the existing techniques. The test case scenarios are provided in order to compare and analyse the proposed framework in the analysis phase. The results of the experiments completed demonstrate the efficacy and success of the proposed framework

Emergent semantics in distributed knowledge management

Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context- and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (e.g., during data interchange) may dramatically increase their business value. This chapter introduce and discuss the notion of Emergent Semantics (ES), where both the representation of semantics and the discovery of the proper interpretation of symbols are seen as the result of a selforganizing process performed by distributed agents, exchanging symbols and adaptively developing the proper interpretation via multi-party cooperation and conflict resolution. Emergent data semantics is dynamically dependent on the collective behaviour of large communities of agents, which may have different and even conflicting interests and agendas. This is a research paradigm interpreting semantics from a pragmatic prospective. The chapter introduce this notion providing a discussion on the principles, research area and current state of the art

Specification of a common framework for characterizing alignment

bouquet2004aNo abstract available

A multi-matching technique for combining similarity measures in ontology integration

Ontology matching is a challenging problem in many applications, and is a major issue for interoperability in information systems. It aims to find semantic correspondences between a pair of input ontologies, which remains a labor intensive and expensive task. This thesis investigates the problem of ontology matching in both theoretical and practical aspects and proposes a solution methodology, called multi-matching . The methodology is validated using standard benchmark data and its performance is compared with available matching tools. The proposed methodology provides a framework for users to apply different individual matching techniques. It then proceeds with searching and combining the match results to provide a desired match result in reasonable time. In addition to existing applications for ontology matching such as ontology engineering, ontology integration, and exploiting the semantic web, the thesis proposes a new approach for ontology integration as a backbone application for the proposed matching techniques. In terms of theoretical contributions, we introduce new search strategies and propose a structure similarity measure to match structures of ontologies. In terms of practical contribution, we developed a research prototype, called MLMAR - Multi-Level Matching Algorithm with Recommendation analysis technique, which implements the proposed multi-level matching technique, and applies heuristics as optimization techniques. Experimental results show practical merits and usefulness of MLMA

Ontology mapping: a logic-based approach with applications in selected domains

In advent of the Semantic Web and recent standardization efforts, Ontology has quickly become a popular and core semantic technology. Ontology is seen as a solution provider to knowledge based systems. It facilitates tasks such as knowledge sharing, reuse and intelligent processing by computer agents. A key problem addressed by Ontology is the semantic interoperability problem. Interoperability in general is a common problem in different domain applications and semantic interoperability is the hardest and an ongoing research problem. It is required for systems to exchange knowledge and having the meaning of the knowledge accurately and automatically interpreted by the receiving systems. The innovation is to allow knowledge to be consumed and used accurately in a way that is not foreseen by the original creator. While Ontology promotes semantic interoperability across systems by unifying their knowledge bases through consensual understanding, common engineering and processing practices, it does not solve the semantic interoperability problem at the global level. As individuals are increasingly empowered with tools, ontologies will eventually be created more easily and rapidly at a near individual scale. Global semantic interoperability between heterogeneous ontologies created by small groups of individuals will then be required. Ontology mapping is a mechanism for providing semantic bridges between ontologies. While ontology mapping promotes semantic interoperability across ontologies, it is seen as the solution provider to the global semantic interoperability problem. However, there is no single ontology mapping solution that caters for all problem scenarios. Different applications would require different mapping techniques. In this thesis, we analyze the relations between ontology, semantic interoperability and ontology mapping, and promote an ontology-based semantic interoperability solution. We propose a novel ontology mapping approach namely, OntoMogic. It is based on first order logic and model theory. OntoMogic supports approximate mapping and produces structures (approximate entity correspondence) that represent alignment results between concepts. OntoMogic has been implemented as a coherent system and is applied in different application scenarios. We present case studies in the network configuration, security intrusion detection and IT governance & compliance management domain. The full process of ontology engineering to mapping has been demonstrated to promote ontology-based semantic interoperability

A METHODOLOGY FOR DEVELOPMENT OF DOMAIN SPECIFIC SIMULATION APPLICATIONS AND ENVIRONMENTS

In the modeling and simulation (M&S) arena, simulation developers have been exploring the concepts that facilitate modeling real world elements using appropriate simulation artifacts within the context of the domain of the application. However, there are some critical issues that distort their effectiveness and efficiency. The first issue is the quantity and quality of assumptions and constraints made during the M&S development, concerning the completeness of simulation models to represent reality. The second issue is the levels of model composability and simulation interoperability, affecting the possibility of data exchange and reusability. The third issue is development of an effective simulation-based environment such that the implementation of the concepts effectively implemented. Thus, this research study aims to develop a methodology that addresses these issues to improve the development of simulation models and the creation of simulation modeling environments particular to specific domains. Conceptual simulation modeling (CSM), model transformation, and domain specific simulation environment (DSSE) create the foundations for this methodology to bridge the gap between reality and simulation

A SAT-Based Algorithm for Context Matching

The development of more and more complex distributed applications over large networks of computers has raised the problem of semantic interoperability across applications based on local and autonomous semantic schemas (e.g., concept hierarchies, taxonomies, ontologies). In this paper we propose to view each semantic schema as a context (in the sense defined in \cite{benerecetti9}), and propose an algorithm for automatically discovering relations across contexts. The main feature of the algorithm is that the problem of finding relationships between contexts is encoded as a problem of logical satisfiability, and so the discovered mappings have a well--defined semantic. The algorithm we describe has been implemented as part of a peer-to-peer system for Distributed Knowledge Management, and tested on significant cases

An ontological approach to information visualization.

Visualization is one of the indispensable means for addressing the rapid explosion of data and information. Although a large collection of visualization techniques have been developed over the past three decades, the majority of ordinary users have little knowledge about these techniques. Despite there being many interactive visualization tools available in the public domain or commercially, producing visualizations remains a skilled and time-consuming task. One approach for cost-effective dissemination of visualization techniques is to use captured expert knowledge for helping ordinary users generate visualizations automatically. In this work, we propose to use captured knowledge in ontologies to reduce the parameter space, providing a more effective automated solution to the dissemination of visualization techniques to ordinary users. As an example, we consider the visualization of music chart data and football statistics on the web, and aim to generate visualizations automatically from the data. The work has three main contributions: Visualisation as Mapping. We consider the visualization process as a mapping task and assess this approach from both a tree-based and graph-based perspective. We discuss techniques for automatic mapping and present a general approach for Information Perceptualisation through mapping which we call Information Realisation. VizThis: Tree-centric Mapping. We have built a tree-based mapping toolkit which provides a pragmatic solution for visualising any XML-based source data using either SVG or X3D (or potentially any other XML-based target format). The toolkit has data cleansing and data analysis features. It also allows automatic mapping through a type-constrained system (AutoMap). If the user wishes to alter mappings, the system gives the users warnings about specific problem areas so that they can be immediately corrected. SeniViz: Graph-centric Mapping. We present an ontology-based pipeline to automatically map tabular data to geometrical data, and to select appropriate visualization tools, styles and parameters. The pipeline is based on three ontologies: a Domain Ontology (DO) captures the knowledge about the subject domain being visualized; a Visual Representation Ontology (VRO) captures the specific representational capabilities of different visualization techniques (e.g.. Tree Map); and a Semantic Bridge Ontology (SBO) captures specific expert-knowledge about valuable mappings between domain and representation concepts. In this way, we have an ontology mapping algorithm which can dynamically score and rank potential visualizations. We also present the results of a user study to assess the validity and effectiveness of the SemViz approach