AROMA Results for OAEI 2009

This paper presents the results obtained by AROMA for its second participation to OAEI. AROMA is an hybrid, extensional and asymmetric ontology alignment method that makes use of the association paradigm and a statistical interestingness measure, the implication intensity. AROMA performs a post-processing step that includes a terminological matcher. This year we modify this matcher in order to improve the recall obtained on real-case ontology, i.e. anatomy and 3xx tests. 1 Presentation of AROMA 1.1 State, purpose, general statement AROMA is an hybrid, extensional and asymmetric matching approach designed to find out relations of equivalence and subsumption between entities, i.e. classes and properties, 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. AROMA relies on the following assumption: An entity Awill be more specific than or equivalent to an entityB 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 represents each entity, i.e. 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 at cleaning and enhancing the resulting alignment. The first stage constructs a set of relevant terms and/or datavalues for each class and property. To do this, we extract the vocabulary of class and property from their annotations and individual values with the help of single and binary term extractor applied to stemmed text. In order to keep a morphism between the partial orders of class and property subsumption hierarchies in one hand and the inclusion of sets of term in the other hand, the terms associated with a class or a property are also associated with its ancestors. 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 means: ”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 rules set. It performs the following tasks: – deduction of equivalence relations, – suppression of cycles in the alignment graph, – suppression of redundant correspondences, – 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 and previously discovered correspondences. This year, we made some changes on the string similarity -based matcher. These changes are primarily designed to improve the recall on anatomy track. Now AROMA includes an equality -based matcher: two entities are considered equivalent if they share at least one annotation. This matcher is only applied on unaligned pairs of entities. The string similarity based matcher still makes use of Jaro-Winkler similarity but relies on a different weighting scheme. As an ontology entity is associated to a set of annotations, i.e. local name, labels and comments, we need a collection measure for aggregating the similarity values between all entity pairs. Last year, we relied on maximal weight maximal graph matching collection measure, see [David and Euzenat, 2008] for details. In order to favour the measure values of most similar annotations pairs, we choose to use the following collection measure: ∆mw(e, e′) =  P a∈T (e) arg maxa′∈T (e′) simjw(a,a ′)2 P a∈T (e) arg maxa′∈T (e′) simjw(a,a ′) if |T (e)| ≤ |T (e ′)| ∆mw(e, e) otherwise where T (e) is the set which contains the annotations and the local name of e, and simjw is the Jaro-Winkler similarity. For all OAEI tracks, we choose a threshold value of 0.8. For more details about AROMA, the reader should refer to [David et al., 2007; David, 2007]. 1.3 Link to the system and parameters file The version 1.1 of AROMA has been used for OAEI2009. This version can be downloaded at : http://gforge.inria.fr/frs/download.php/23649/AROMA-1.1.zip. The command line used for aligning two ontologies is: java -jar aroma.jar onto1.owl onto2.owl [alignfile.rdf] The resulting alignment is provided in the alignment format. 1.4 Link to the set of provided alignments (in align format) http://www.inrialpes.fr/exmo/people/jdavid/oaei2009/results_AROMA_oaei2009.zip