AROMA Results for OAEI 2008

This paper presents the results obtained by AROMA for its first participation to OAEI. AROMA is an hybrid, extensional and asymmetric ontology alignment method which makes use of the association paradigm and a statistical interstingness measure, the implication intensity. 1 Presentation of AROMA 1.1 State, purpose, general statement AROMA is an hybrid, extensional and asymmetric matching approach designed to find out relations (equivalence and subsumption) between entities issued from two textual taxonomies (web directories or OWL ontologies). Our approach makes use of the association rule paradigm [Agrawal et al., 1993], and a statistical interestingness measure used in this context. AROMA relies on the following assumption: An entity A will be more specific than or equivalent to an entity B if the vocabulary (i.e. terms and also data) used to describe A, its descendants, and its instances tends to be included in that of B. 1.2 Specific techniques used AROMA is divided into three successive main stages: (1) The pre processing stage allows to represent each entity (classes and properties) by a set of terms, (2) the second stage consists of the discovery of association rules between entities, and finally (3) the post processing stage aims to clean and enhance the alignment. The first stage constructs a set of relevant terms and/or datavalues for each entity. To do this, we extract the vocabulary of entities from their annotations and individual values with the help of single and binary term extractor applied to stemmed text. The second stage of AROMA discovers the subsumption relations by using the association rule model and the implication intensity measure [Gras et al., 2008]. In the context of AROMA, an association rule a → b represents a quasi-implication (i.e. an implication allowing some counter-examples) from the vocabulary of entity a into the vocabulary of the entity b. Such a rule could be interpreted as a subsumption relation from the antecedent entity toward the consequent one. For example, the binary rule car → vehicle could be interpret: ”The concept car is more specific than the concept vehicle”. The rule extraction algorithm takes advantage of the partial order structure provided by the subsumption relation, and a property of the implication intensity for pruning the search space. The last stage concerns the post processing of the association rule set. It performs the following tasks: – the deduction of equivalence relations, – the suppression of cycles in the alignment graph, – the suppression of the redundant correspondences, – the selection of the best correspondence for each entity (the alignment is an injective function), – the enhancement of the alignment by using a string similarity -based matcher (JaroWinkler similarity) and previously discovered correspondences. For more details, the reader should refer to [David et al., 2007; David, 2007]. 1.3 Link to the system and parameters file The version of AROMA used for OAEI 2008 is available at: http://www.inrialpes.fr/exmo/people/jdavid/oaei2008/AROMA_oaei2008.jar. The source code is available at : http://www.inrialpes.fr/exmo/people/jdavid/oaei2008/AROMAsrc_oaei2008.jar For align two ontologies use the following command line: java -jar AROMA_oaei2008.jar onto1.owl onto2.owl The resulting alignment is provided on the standard output in the alignment format. 1.4 Link to the set of provided alignments (in align format) http://www.inrialpes.fr/exmo/people/jdavid/oaei2008/results_AROMA_oaei2008.zip