Learning domain ontologies for Web service descriptions

Abstract

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in my Grid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.

Automatic Web Service Tagging Using Machine Learning and WordNet Synsets

“…They are also useful to have a quick understanding of a service without reading the whole description. They can also be used to help in building domain ontologies like in [24] [14], also in tasks such as service clustering (for instance by measuring the similarity of the tags of two given services), or classification (for instance by defining association rules between tags and categories). One of our perspectives is to work on extracting composed tags, which consist of more than one word.…”

Bootstrapping Domain Ontology for Semantic Web Services from Source Web Sites

“…[17]). Further, the ontology learned with our approach can be utilized to train concept and instance classifiers, which can then be employed to markup the Web services by the approaches such as [15].…”

“…Our work is most closely related to [17], but different in several aspects. First, [17] learns domain ontology from the documentations which might accompany the descriptions of Web services, while our work exploits the information from the source Web sites.…”

“…Our work is most closely related to [17], but different in several aspects. First, [17] learns domain ontology from the documentations which might accompany the descriptions of Web services, while our work exploits the information from the source Web sites. Second, we extract concepts and instances from semistructured data over source interfaces and data pages, while [17] learns ontology from natural language texts.…”

“…First, [17] learns domain ontology from the documentations which might accompany the descriptions of Web services, while our work exploits the information from the source Web sites. Second, we extract concepts and instances from semistructured data over source interfaces and data pages, while [17] learns ontology from natural language texts. [15] proposes METEOR S, a framework for annotating WSDL files with concepts from an existing domain ontology.…”