MaPLE: A MapReduce Pipeline for Lattice-based Evaluation and Its Application to SNOMED CT.

Zhang GQ, Zhu W, Sun M, Tao S, Bodenreider O, Cui L

Proc IEEE Int Conf Big Data. 2014 Oct;2014:754-759.

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Abstract:

Non-lattice fragments are often indicative of structural anomalies in ontological systems and, as such, represent possible areas of focus for subsequent quality assurance work. However, extracting the non-lattice fragments in large ontological systems is computationally expensive if not prohibitive, using a traditional sequential approach. In this paper we present a general MapReduce pipeline, called MaPLE (MapReduce Pipeline for Lattice-based Evaluation), for extracting non-lattice fragments in large partially ordered sets and demonstrate its applicability in ontology quality assurance. Using MaPLE in a 30-node Hadoop local cloud, we systematically extracted non-lattice fragments in 8 SNOMED CT versions from 2009 to 2014 (each containing over 300k concepts), with an average total computing time of less than 3 hours per version. With dramatically reduced time, MaPLE makes it feasible not only to perform exhaustive structural analysis of large ontological hierarchies, but also to systematically track structural changes between versions. Our change analysis showed that the average change rates on the non-lattice pairs are up to 38.6 times higher than the change rates of the background structure (concept nodes). This demonstrates that fragments around non-lattice pairs exhibit significantly higher rates of change in the process of ontological evolution.

Zhang GQ, Zhu W, Sun M, Tao S, Bodenreider O, Cui L. MaPLE: A MapReduce Pipeline for Lattice-based Evaluation and Its Application to SNOMED CT. 
Proc IEEE Int Conf Big Data. 2014 Oct;2014:754-759.

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