Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent

In domains such as biomedicine, ontologies are prominently utilized for annotating data. Consequently, aligning ontologies facilitates integrating data. Several algorithms exist for automatically aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time without compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the community in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible.