IAMA results for OAEI 2013

This paper presents the results of IAMA on OAEI 2013. IAMA (Institute of Automation’s Matcher) is an ontology matching system with the capability to deal with large scale ontologies. IAMA is designed to find out the correspondences between two ontologies by using multiple similarity measures. Candidate filtering technique is adopted when processing ontologies at large scale. 1 Presentation of the system 1.1 State, purpose, general statement Large amount of ontologies has been published since the semantic web emerged. However, managing the heterogeneity among various ontologies is still a problem [1]. For example, many ontologies describe the same entity (i.e., class or property) using different terminologies, while the entities having the same name belonging to different ontologies may refer to disparate objects. Finding the matching between different ontologies is still challenging. Ontology matching, as a solution to the aforementioned problem, has received great interests in these years. The principal goal of IAMA is to discover equivalent entities rapidly between different ontologies. We use efficient terminology matching techniques and do not turn to any external resource at this stage. IAMA is able to match classes and properties of two input ontologies. The system could achieve qualified results, though neglecting the structural information. The Matching process takes little time to cope with small ontologies. When processing large scale ontologies, IAMA could still, with the help of candidate filtering, yield the alignment in reasonable time. We tend to make an universal and extensible system, so more matching methods could be conveniently incorporated in the future. 1.2 Specific techniques used IAMA employs various similarity measures to take advantage of the available information in the ontologies. The entities in two ontologies are pairwise compared, and lexical similarities and structural similarities are calculated respectively. In the current version, only 1:1 alignment is considered. Let O1 and O2 denote the two input ontologies, and e1 is an entity in O1. Each entity e2 in O2 has a similarity with e1 indicated as sim(e1, e2). We are able to find out the maximum value as sim(e1, ê). If sim(e1, ê) is greater than a predetermined threshold t1, entity pair (e1, ê) will be added to the alignment. In the following paragraphs, we will present the used similarity measures in our system. Lexical Similarity The system extracts local names, labels, and comments of the entities in the two input ontologies as lexical features. For most situations, the lexical information is effective. Local Name similarity measures the similarity between the names of two entities. We get rid of the spaces and other punctuations because the entity name is comprised of multiple words or contains hyphens at times. All the letters are turned to lower case simultaneously. Label Similarity measures the similarity between the labels. Not all the entities have labels, and many entities have a label exactly the same as its local name. Comment Similarity measures the similarity between the comments. A comment of an entity is usually a brief descriptive sentence, which is helpful when the two ontologies name their entities with quite different style. Both labels and comments are processed as local names, thereby treated as a single word. IAMA uses Levenshtein [2] distance, which is proved competent in [3], to calculate lexical similarities. For the three lexical similarities mentioned above, we do not take them equally. Each similarity is assigned a weight intuitively. Local name similarity has a greater weight than label similarity, while comments similarity has the lowest weight. Individual Similarity Between the classes that have individuals, Individual Similarity is additionally calculated. The names of individuals that belong to a class are extracted to a set of string. Assume S1 and S2 are two sets, then the similarity between them is computed as follows: sim(S1, S2) = 2× #(S1 ∩ S2) #S1 +#S2 (1) For example, if c1 is a class in ontology O1, and c2 is a class in ontology O2. The names of the individuals belonging to c1 is a set of string i1 = {s1, s2, s3}, and similarly we get i2 = {s2, s3, s4, s5}. The individual similarity simi(c1, c2) is: simi(c1, c2) = 2× #(i1 ∩ i2) #i1 +#i2 = 2× 2 3 + 4 = 0.571 IAMA adopts the maximum value of all the similarities as the final similarity of the entity pair. It is worth noting that other similarities such as superclass similarity, subclass similarity, domain similarity and range similarity are also tested in our earlier attempts. But they contributed little considering the time increased. They could be added easily if needed, which makes IAMA extensible.