Ontology acquisition from web service descriptions

Abstract

The lack of formally expressed semantics in web services complemented with the increasing number of available web services is the main obstacle in analyzing and using the existing web services exposed in the Web. In the absence of appropriate reference domain ontology, annotation of existing web services is dependent on ontology development and ontology learning techniques. In this paper we present an unsupervised ontology learning approach tailored to learning from WSDL documents. The most specific feature of the suggested approach is that it constructs (semi-) automatically ontology fragments from a collection of WSDL documents, that lack any extra textual documentation, by just exploiting element names in the WSDL document. The suggested approach combines both linguistic and statistic analysis techniques such as lexico-syntactic patterns and term co-occurrence analysis. The preliminary results show that the generated ontology captures correctly more than half of the semantic classes and instances as well as taxonomic and non-taxonomic relations, hence, providing a reasonable basis for automatic web services annotation.

Extracting Knowledge From English Translated Quran Using NLP Pattern

“…The clustering techniques find the similarity of terms to be grouped together. This process will discover concepts or construct hierarchy [33]. The process of clustering can either create agglomerative clustering or create divisive clustering.…”