52,965 research outputs found
Recommended from our members
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
Recommended from our members
Visual support for ontology learning: an experience report
Ontology learning methods aim to automate ontology
construction. They are complex methods involving several
elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where
understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them
Constructive Ontology Engineering
The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated and semi-automated processes. Due to the vast amount of unstructured text and its continued proliferation, the problem of constructing ontologies from text has attracted considerable attention for research. However, the constructed ontologies may be noisy, with missing and incorrect knowledge. Thus ontology construction continues to be a challenging research problem. The goal of this research is to investigate a new method for guiding a process of extracting and assembling candidate terms into domain specific concepts and relationships. The process is part of an overall semi automated system for creating ontologies from unstructured text sources and is driven by the user’s goals in an incremental process. The system applies natural language processing techniques and uses a series of syntactical analysis tools for extracting grammatical relations from a list of text terms representing the parts of speech of a sentence. The extraction process focuses on evaluating the subject predicate-object sequences of the text for potential concept-relation-concept triples to be built into an ontology. Users can guide the system by selecting seedling concept-relation-concept triples to assist building concepts from the extracted domain specific terms. As a result, the ontology building process develops into an incremental one that allows the user to interact with the system, to guide the development of an ontology, and to tailor the ontology for the user’s application needs. The main contribution of this work is the implementation and evaluation of a new semi- automated methodology for constructing domain specific ontologies from unstructured text corpus
Ontology technology for the development and deployment of learning technology systems - a survey
The World-Wide Web is undergoing dramatic changes at the moment. The Semantic Web is an initiative to bring meaning to the Web. The Semantic Web is based on ontology
technology – a knowledge representation framework – at its core. We illustrate the importance of this evolutionary development. We survey five scenarios demonstrating different forms of applications of ontology technologies in the development and deployment of learning technology
systems. Ontology technologies are highly useful to organise, personalise, and publish learning content and to discover, generate, and compose learning objects
- …