Ontological model of virtual community of practice (VCoP) participation : a case of research group community in higher learning institution

The increasing number of Virtual Communities of Practice (VCoP) leads to the needs of having an ontological model in mapping new members (new researchers) to their relevant Research Group(s) especially in Higher Learning Institution. This paper proposes a new model of ontology of Virtual Community of Practice (VCoP) called Ontology-based VCoP (Onto-VCoP) that can help new member of researcher to identify themselves for the suitability of joining research groups efficiently. The efficiency of our model is based on mapping technique that was adopted from Ehrig and Staab [1] Quick Ontology Mapping (QOM). Onto-VCoP model applied ontology to represent knowledge and QOM to map data between new researchers and research groups. Systematically reviewed for literature and pre-survey is done to get the user requirement and to support objective of this paper. The result shows Onto-VCoP model may help new researchers to identify research groups based on their research interest efficiently

Gap analysis of ontology mapping tools and techniques

Mapping between ontologies provides a way to overcome any dissimilarities in the terminologies used in two ontologies. Some tools and techniques to map ontologies are available with some semi-automatic mapping capabilities. These tools are employed to join the similar concepts in two ontologies and overcome the possible mismatches.Several types of mismatches have been identified by researchers and certain overlaps can easily be seen in their description. Analysis of the mapping tools and techniques through a mismatches framework reveals that most of the tools and techniques just target the explication side of the concepts in ontologies and a very few of them opt for the conceptualization mismatches. Research therefore needs to be done in the area of detecting and overcoming conceptualization mismatches that may occur during the process of mapping. The automation and reliability of these tools are important because they directly affect the interoperatbility between different knowledge sources

Machine Learning-Based Ontology Mapping Tool to Enable Interoperability in Coastal Sensor Networks

In today’s world, ontologies are being widely used for data integration tasks and solving information heterogeneity problems on the web because of their capability in providing explicit meaning to the information. The growing need to resolve the heterogeneities between different information systems within a domain of interest has led to the rapid development of individual ontologies by different organizations. These ontologies designed for a particular task could be a unique representation of their project needs. Thus, integrating distributed and heterogeneous ontologies by finding semantic correspondences between their concepts has become the key point to achieve interoperability among different representations. In this thesis, an advanced instance-based ontology matching algorithm has been proposed to enable data integration tasks in ocean sensor networks, whose data are highly heterogeneous in syntax, structure, and semantics. This provides a solution to the ontology mapping problem in such systems based on machine-learning methods and string-based methods

Survey on Techniques for Ontology Interoperability in Semantic Web

Ontology is a shared conceptualization of knowledge representation of particular domain. These are used for the enhancement of semantic information explicitly. It is considered as a key element in semantic web development. Creation of global web data sources is impossible because of the dynamic nature of the web. Ontology Interoperability provides the reusability of ontologies. Different domain experts and ontology engineers create different ontologies for the same or similar domain depending on their data modeling requirements. These cause ontology heterogeneity and inconsistency problems. For more better and precise results ontology mapping is the solution. As their use has increased, providing means of resolving semantic differences has also become very important. Papers on ontology interoperability report the results on different frameworks and this makes their comparison almost impossible. Therefore, the main focus of this paper will be on providing some basics of ontology interoperability and briefly introducing its different approaches. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies and its related techniques and tools

Current State of Ontology Matching. A Survey of Ontology and Schema Matching

Ontology matching is an important task when data from multiple data sources is integrated. Problems of ontology matching have been studied widely in the researchliterature and many different solutions and approaches have been proposed alsoin commercial software tools. In this survey, well-known approaches of ontologymatching, and its subtype schema matching, are reviewed and compared. The aimof this report is to summarize the knowledge about the state-of-the-art solutionsfrom the research literature, discuss how the methods work on different application domains, and analyze pros and cons of different open source and academic tools inthe commercial world.Siirretty Doriast

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences Among Ontologies

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'sability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to mapspecific items in one domain to specific items in another domain. Representation layer helpsthe network learn relationship mapping with direct training method.The OMNN approach is tested on family tree test cases. Node mapping, relationshipmapping, unequal structure mapping, and scalability test are performed. Results showthat OMNN is able to learn and infer correspondences in tree-like structures. Furthermore, OMNN is applied to several OAEI benchmark test cases to test its performance on ontologymapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009

Ontology Alignment: An annotated Bibliography

Ontology mapping, alignment, and translation has been an active research component of the general research on semantic integration and interoperability. In our talk, we gave our own classification of different topics in this research. We talked about types of heterogeneity between ontologies, various mapping representations, classified methods for discovering methods both between ontology concepts and data, and talked about various tasks where mappings are used. In this extended abstract of our talk, we provide an annotated bibliography for this area of research, giving readers brief pointers on representative papers in each of the topics mentioned above. We did not attempt to compile a comprehensive bibliography and hence the list in this abstract is necessarily incomplete. Rather, we tried to sketch a map of the field, with some specific reference to help interested readers in their exploration of the work to-date

Exploiting conceptual spaces for ontology integration

The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards – based on first-order logic (FOL) and syllogistic reasoning – does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances – represented as members in CS – is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration