A Framework for Resource Annotation and Classification in Bioinformatics

Semantic annotation is commonly recognized as one of the cornerstones of the semantic Web. In the context of Web services, semantic annotations can support effective and efficient discovery of services, and guide their composition into workflows. Because semantic annotation is a time consuming and expensive task, (semi-)automatic approaches for semantic annotation extraction are required. In this paper, we propose a semi-automatic extraction approach of lightweight semantic annotations from textual description of Web services. In contrast with most of the existing semi-automatic approaches for semantic annotations of Web services which rely on a predefined domain ontology, we investigate the use of NLP techniques to derive service properties given a corpus of textual description of bioinformatics services. We evaluate the performance of the annotation extraction method and the importance of lightweight annotations to classify bioinformatics Web services in order to bootstrap the service discovery process. Our framework relies an unsupervised clustering approach based on a simultaneous clustering algorithm that enables to determine biclusters of Web services and semantic annotations highly correlated