Alignment results of anchor-flood algorithm for OAEI-2008

Abstract

Abstract. Our proposed algorithm called Anchor-Flood algorithm, starts off with anchors. It gradually explores concepts by collecting neighbors in concept taxonomy, thereby taking advantage of locality of reference in the graph data structure. Then local alignment process runs over the collected small blocks of concepts. The process is repeated for the newly found aligned pairs. In this way, we can significantly reduce the computational time for the alignment as our algorithm concentrates on the aligned pairs and it resolves the scalability problem in ontology alignment over large ontologies. Through several experiments against OAEI-2008 datasets, we will demonstrate the results and the features of our Anchor-Food algorithm. 1 Presentation of the system The Anchor-Flood algorithm is mainly designed targeting to align two large scale ontologies or one large scale and another small scale ontologies effectively. It does not compare an entity against all the entities in other ontology. The way of selecting the group of entities to be compared is the novelty of our algorithm. Our algorithm operates quite faster over large ontologies as observed in aligning anatomy ontologies and it is depicted in Table 2. 1.1 State, purpose, general statement The purpose of our Anchor-Flood algorithm is basically ontology matching. However, we used our algorithm in patent mining system to classify a research abstract in terms of International Patent Classification (IPC). Containing mostly general terminologies leads classifying an abstract a formidable task. Automatic extracted taxonomy of related terms available in an abstract is aligned with the taxonomy of IPC ontology with our algorithm succesfully. We also start using the Anchor-Flood in the focus-oriented biomedical applications which generally contain very large ontologies. To be specific, we only describe our Anchor-Flood algorithm and the results against OAEI 2008 datasets here. For more details, we refer the reader to our semantic website