Abstract
In many areas of life science, such as biology and medicine, ontologies are nowadays commonly used to annotate objects of interest, such as biological samples, clinical pictures, or species in a standardized way. In these applications, an ontology is merely a structured vocabulary in the form of a tree or a directed acyclic graph of concepts. Typically, ontologies are stored together with the data they annotate in relational databases. Querying such annotations must obey the special semantics encoded in the structure of the ontology, i.e. relationships between terms, which is not possible using standard SQL alone.
In this paper, we develop a new method for querying DAGs using a pre-computed index structure. Our new indexing method extends the pre-/ postorder ranking scheme, which has been studied intensively for trees, to DAGs. Using typical queries on ontologies, we compare our approach to two other commonly used methods, i.e., a recursive database function and the pre-computation of the transitive closure of a DAG.
We show that pre-computed indexes are an order of magnitude faster than recursive methods. Clearly, our new scheme is slower than usage of the transitive closure, but requires only a fraction of the space and is therefore applicable even for very large ontologies with more than 200,000 concepts.
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Trißl, S., Leser, U. (2005). Querying Ontologies in Relational Database Systems. In: Ludäscher, B., Raschid, L. (eds) Data Integration in the Life Sciences. DILS 2005. Lecture Notes in Computer Science(), vol 3615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11530084_7
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DOI: https://doi.org/10.1007/11530084_7
Publisher Name: Springer, Berlin, Heidelberg
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