AbstractsBiology & Animal Science

Semantic annotation of Web services is achieved by associating parts of service interface descriptions (e.g., operations, parameters and datatypes) to concepts defined in a semantic model, such as an ontology. Annotating Web services with semantic metadata is currently a manual procedure that is conducted by human annotators. In practice, however, service annotation can be both time-consuming and error-prone to implement on real-world Web services. We have investigated this problem in the bioinformatics domain, where certain characteristics shared by many Web services make them a difficult breed to annotate with semantics. By taking these characteristics into consideration, in this thesis we describe a probing-based technique that identifies upper and lower semantic bounds for the annotation of Web service input parameters. The output bounds can serve as a useful starting point for human annotators and could help reduce the required effort in the semantic annotation process. Our experiment results showed the probing-based technique was overall able to produce better candidate annotation sets in comparison with an existing string similarity matching based annotation tool.