LILY: the Results for the Ontology Alignment Contest OAEI 2007

This paper presents the results of LILY, which is an ontology mapping system, for OAEI 2007 campaign. To accurately describe what the real meaning of an entity in the original ontology is, LILY extracts a semantic subgraph for each entity. Then it exploits both linguistic and structural information in semantic subgraphs to generate initial alignments. If necessary, using these initial results as input, a subsequent similarity propagation strategy could produce more alignments, which often can not be obtained by the previous process. The preliminary results of the experiments for four tasks (i.e. benchmark, directories, anatomy and conference) are presented. The discussion of the results and future work of LILY are also given. 1 Presentation of the system Currently more and more ontologies are distributedly used and built by different communities. Many of these ontologies would describe similar domains, but using different terminologies, and others will have overlapping domains. Such ontologies are referred to as heterogeneous ontologies, which is a major obstacle to realize semantic interoperation. Ontology mapping, which captures relations between ontologies, aims to provide a common layer from which heterogeneous ontologies could exchange information in semantically sound manners. LILY is a system for solving the issues related to heterogeneous ontologies. One important function of LILY is to match heterogeneous ontologies. LILY uses the semantic subgraph to describe the meaning of an entity. Then linguistic and structural similarity algorithm and similarity propagation strategy are exploited to create the alignments between ontologies. 1.1 State, purpose, general statement When LILY is used to find alignments between heterogeneous ontologies, it tries to utilize all useful information to discover the correct matching results. Currently it does not use any external knowledge such as WordNet. The matching process consists of three main steps: (1) Extracting semantic subgraph LILY tries to use a semantic subgraph to represent the real meaning for a given entity in an ontology. A semantic subgraph, which is also a subgraph of the original ontology, is extracted by a variant algorithm based on the connection subgraphs discovery algorithm [1]. (2) Computing alignment similarity Through analyzing the literal and structural information in the semantic subgraphs, LILY computes the similarity confidences between entities from different ontologies. (3) Similarity propagation In most cases, LILY can find satisfactory alignment results after the second process. If few alignment results are got, a strategy will decide whether to take similarity propagation process. The similarity propagation could produce more alignments that can not be found in the previous processes. The matching process is shown in Fig. 1. LILY is still being improved and enhanced, and the lasted version is V1.2. 1.2 Specific techniques used LILY aims to provide high quality alignments between concept/property pairs. The main specific techniques used by LILY are as follows. Semantic subgraph An entity in a given ontology has its specific meaning. In our ontology mapping view, capturing such meaning is very important to obtain good alignment results. Therefore, before similarity computation, LILY first describes the meaning for each entity accurately. The solution is inspired by the method proposed by Faloutsos et al. for discovering connection subgraphs [1]. It is based on electricity analogues to extract a small subgraph that best captures the connections between two nodes of the graph. Ramakrishnan et al. also exploits such idea to find the informative connection subgraphs in RDF graph. We modify the method for extracting an n-size subgraph for a node or edge in an ontology graph. The subgraphs can give the precise descriptions of the meanings of the entities, and we call such subgraphs semantic subgraphs. The details of the semantic subgraph extraction process will be reported elsewhere. Alignment similarity computation The similarity computation is based on the semantic subgraphs, i.e. all the information used in the similarity computation is come from the semantic subgraphs. LILY uses two kinds of descriptions to interpret the concepts and properties. The first is the basic description, which is a document consisting of the identifier, label and comments. The second is the semantic description. A semantic description of a concept contains the information about class hierarchies, related properties and instances. A semantic description of a property contains the information about hierarchies, domains, ranges, restrictions and related instances. For the descriptions from different entities, we calculate the similarities of the corresponding parts. Finally, all separate similarities are combined with the experiential weights. The descriptions collect the linguistic and structural information of entities. Therefore, for the regular ontologies, LILY can find satisfactory alignments in most cases. Fig. 1. Matching process Extracting