Alignment Results of Anchor-Flood Algorithm for OAEI-2008

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 OAEI2008 datasets, we will demonstrate the results and the features of our AnchorFood 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 : http://www.kde.ics.tut.ac.jp/h̃anif. More elaborate information will be come out soon in our semantic technology geared website. 1.2 Specific techniques used We implemented Anchor -Flood algorithm in java. Our algorithm contains preprocessing, adaptation module for OAEI 2008, the basic block of algorihtm and the local alignment process. We created our own persistent model of ontology, as our algorithm requires optimal graph structure of concept taxonomy along with other non-trivial structural and simple lexical information. To collect the necessary information in repository, we use the ARP triple parser of jena module. Fig 1 shows the basic block of Anchor-Flood algorithm to comprehend easily. However, it has complex process of collecting small blocks of concepts and related properties dynamically. As a part of preprocessing, we also normalize the lexical information and extract the derivative relations, like inherited restrictions etc. The basic part of Anchor-Flood algorithm is depicted in Fig. 1. Starting off an anchor, Anchor-Flood algorithm collects neighboring concepts which includes super concepts, siblings and subconcepts of certain depth to form a pair of blocks across ontologies, as the neighbors of similar concepts might also be similar [5]. Local alignment process aligns concepts and their related properties based on lexical information [2, 7, 8], semantic information [4] and structural relations [1, 3, 4]. Found aligned pairs are considered for further processing. Hence, it burst out with a pair of aligned block in a compacked part of the ontologies, giving the taste of segmentation [6]. Multiple anchors from different part of ontologies confirm a fair collection of aligned pairs as a whole. 1.3 Adaptations made for the evaluation The Anchor-Flood algorithm needs an anchor to start off. Therefore, we used another tiny program module, which is capable of extarcting some probable aligned pairs as anchors. The tiny program is attached inside along with our basic algorithm to produce a system. It uses lexical information and some statistical relational information to extract a small number of aligned pairs from different part of ontologies. The program is essentially small, simple and faster. We also removed the subsumption module of our algorithm to make it more faster. 1.4 Link to the system and parameters file The version of Anchor-Flood for OAEI-2008 can be downloaded from our website: http://www.kde.ics.tut.ac.jp/h̃anif/res/anchor flood.zip 1.5 Link to the set of provided alignments (in align format) The results for OAEI-2008 are available at our website: http://www.kde.ics.tut.ac.jp/ h̃anif/res/aflood.zip