Finding Answers and Generating Explanations for Complex Biomedical Queries

Some of these complex queries, such as Q1 or Q2, Recent advances in health and life sciences have led to generation can be represented in a formal query language (e.g., of a large amount of biomedical data. To facilitate access SQL/SPARQL) and then answered using Semantic Web to its desired parts, such a big mass of data has been represented technologies. However, queries, like Q4, that require auxiliary in structured forms, like biomedical ontologies and recursive definitions (such as transitive closure) cannot databases. On the other hand, representing these biomedical be directly represented in these languages; and thus such ontologies and databases in different forms, constructing queries cannot be answered directly using Semantic Web them independently from each other, and storing them at technologies. The experts usually compute auxiliary relations different locations have brought about many challenges for externally, for instance, by enumerating all drug-drug answering queries about the knowledge represented in these interaction chains or gene cliques, and then use these auxiliary ontologies and databases.