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Ontology learning for semantic web services
Semantic Web Services promise automatic service discovery and composition, relying heavily on domain ontology as a core component. With large Web Service repository, manual ontology development is proving a bottleneck (with associated expense and likely errors) to the realisation of a semantic Web of services. Providing the appropriate tools that assist in and automate ontology development is essential for a dynamic service vision to be realised. As a statement of research-in-progress, this paper proposes combining different ontology learning paradigms in Web Services domain, highlighting the need for further research that accommodates the variation in Web Service descriptive and operational sources. A research agenda is proposed that recognises this variation in artefacts as they are selected, pre-processed and analyzed by ontology learning techniques
Infraestructura tecnolĂłgica de servicios semĂĄnticos para la Web SemĂĄntica
This project aims at creating a network of distributed interoperable semantic services for
building more complex ones. These services will be available in semantic Web service
libraries, so that they can be invoked by other systems (e.g., semantic portals, software
agents, etc.). Thus, to accomplish this objective, the project proposes:
a) To create specific technology for developing and composing Semantic Web Services.
b) To migrate the WebODE ontology development workbench to this new distributed
interoperable semantic service architecture.
c) To develop new semantic services (ontology learning, ontology mappings,
incremental ontology evaluation, and ontology evolution).
d) To develop technological support that eases semantic portal interoperability, using
Web services and Semantic Web Services.
The project results will be open source, so as to improve their technological transfer. The
quality of these results is ensured by a benchmarking process.
Keywords: Ontologies and Semantic We
Building Effective Ontology for Semantic Web: a Discussion Based on Practical Examples
The study aims to investigate semantic web and create useful ontology as a teaching and educational tool for others interested in learning more about Semantic web. This paper discussed several emerging issues about the semantic web and ontology building. This paper combines ontology implementation examples with research topics to identify current issues and potential solution in both application and theoretical level. It concludes that although semantic web and ontology technology are not mature enough currently, there is a clear tendency for them to be integrated into various applications to exert synergies
Evaluation of the Project Management Competences Based on the Semantic Networks
The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.
Ontology learning for the semantic deep web
Ontologies could play an important role in assisting users in their search for Web pages. This dissertation considers the problem of constructing natural ontologies that support users in their Web search efforts and increase the number of relevant Web pages that are returned. To achieve this goal, this thesis suggests combining the Deep Web information, which consists of dynamically generated Web pages and cannot be indexed by the existing automated Web crawlers, with ontologies, resulting in the Semantic Deep Web. The Deep Web information is exploited in three different ways: extracting attributes from the Deep Web data sources automatically, generating domain ontologies from the Deep Web automatically, and extracting instances from the Deep Web to enhance the domain ontologies. Several algorithms for the above mentioned tasks are presented. Lxperimeiital results suggest that the proposed methods assist users with finding more relevant Web sites. Another contribution of this dissertation includes developing a methodology to evaluate existing general purpose ontologies using the Web as a corpus. The quality of ontologies (QoO) is quantified by analyzing existing ontologies to get numeric measures of how natural their concepts and their relationships are. This methodology was first applied to several major, popular ontologies, such as WordNet, OpenCyc and the UMLS. Subsequently the domain ontologies developed in this research were evaluated from the naturalness perspective
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Ontology learning for Semantic Web Services
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 18/10/2010.The expansion of Semantic Web Services is restricted by traditional ontology engineering methods. Manual ontology development is time consuming, expensive and a resource exhaustive task. Consequently, it is important to support ontology engineers by
automating the ontology acquisition process to help deliver the Semantic Web vision.
Existing Web Services offer an affluent source of domain knowledge for ontology
engineers. Ontology learning can be seen as a plug-in in the Web Service ontology
development process, which can be used by ontology engineers to develop and maintain
an ontology that evolves with current Web Services. Supporting the domain engineer
with an automated tool whilst building an ontological domain model, serves the purpose
of reducing time and effort in acquiring the domain concepts and relations from Web
Service artefacts, whilst effectively speeding up the adoption of Semantic Web Services, thereby allowing current Web Services to accomplish their full potential. With that in mind, a Service Ontology Learning Framework (SOLF) is developed and
applied to a real set of Web Services. The research contributes a rigorous method that
effectively extracts domain concepts, and relations between these concepts, from Web
Services and automatically builds the domain ontology. The method applies pattern-based
information extraction techniques to automatically learn domain concepts and
relations between those concepts. The framework is automated via building a tool that implements the techniques. Applying the SOLF and the tool on different sets of services results in an automatically built domain ontology model that represents semantic knowledge in the underlying domain. The framework effectiveness, in extracting domain concepts and relations, is evaluated
by its appliance on varying sets of commercial Web Services including the financial domain. The standard evaluation metrics, precision and recall, are employed to determine both the accuracy and coverage of the learned ontology models. Both the
lexical and structural dimensions of the models are evaluated thoroughly. The evaluation results are encouraging, providing concrete outcomes in an area that is little researched
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing
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