A semantic approach for the requirement-driven discovery of web resources in the Life Sciences

Research in the Life Sciences depends on the integration of large, distributed and heterogeneous web resources (e.g., data sources and web services). The discovery of which of these resources are the most appropriate to solve a given task is a complex research question, since there are many candidate resources and there is little, mostly unstructured, metadata to be able to decide among them. In this paper, we contribute to a semi-automatic approach, based on semantic techniques, to assist researchers in the discovery of the most appropriate web resources to fulfill a set of requirements. The main feature of our approach is that it exploits broad knowledge resources in order to annotate the unstructured texts that are available in the emerging web-based repositories of web resource metadata. The results show that the web resource discovery process benefits from a semantic-based approach in several important aspects. One of the advantages is that the user can express her requirements in natural language avoiding the use of specific vocabularies or query languages. Moreover, the discovery exploits not only the categories or tags of web resources, but also their description and documentation.