An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N^2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology. Our proposed algorithm called Anchor-Flood algorithm, boasting of O([email protected]?(N)) computation on the average, starts off with an anchor, a pair of ''look-alike'' concepts from each ontology, gradually exploring concepts by collecting neighboring concepts, thereby taking advantage of locality of reference in the graph data structure. It outputs a set of alignments between concepts and properties within semantically connected subsets of two entire graphs, which we call segments. When similarity comparison between a pair of nodes in the directed graph has to be made to determine whether two given ontologies are aligned or not, we repeat the similarity comparison between a pair of nodes, within the neighborhood pairs of two ontologies surrounding the anchor iteratively until the algorithm meets that ''either all the collected concepts are explored, or no new aligned pair is found''. In this way, we can significantly reduce the computational time for the alignment. Moreover, since we only focus on segment-to-segment comparison, regardless of the entire size of ontologies, our algorithm not only achieves high performance, but also resolves the scalability problem in aligning ontologies. Our proposed algorithm reduces the number of seemingly-aligned but actually misaligned pairs. Through several examples with large ontologies, we will demonstrate the features of our Anchor-Food algorithm.