Querying Semantically Web Proteomics Sources: A Logical Perspective

Querying and sharing Web proteomics data is a great and challenging topic in Pharmaceutical Drug Discovery and Computational Biology. Given that, several data sources can be used to answer the same sub-goals in the Global query, it is obvious that we can have many different candidates rewritings. The user-query is formulated using Concepts and Properties related to Proteomics research (Domain Ontology). Semantic mappings describe the contents of underlying sources in order to highlight their query capabilities. In recent works, we have proposed a characterization of query rewriting problem using semantic mappings as an associated hypergraph. Hence, the generation of candidates´ rewritings can be formulated as the discovery of minimals Transversals from this hypergraph. We exploit and adapt some algorithms available in Hypergraph Theory to find all candidates rewritings from a query answering problem, and so to rank these rewritings according to User preferences, and sources technical performances. This current paper extends our research works by proposing a language, called ψ-trees and based on ψ-terms logical formalisms in order to capture semantics of proteomics knowledges and better discover biological resources available on the Web.