Aroma

AROMA is an extensional and scalable ontology matcher designed to find out relations of equivalence and subsumption between entities, i.e. classes and properties, issued from two textual taxonomies or OWL ontologies. It makes use of the association rule paradigm and a statistical interestingness measure, the implication intensity. 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". AROMA is divided into three successive main stages: (1) The pre processing stage represents each entity, 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. In the context of AROMA, an association rule a \rightarrow b represents a quasi-implicationan association rule a \rightarrow b represents a quasi-implicationi.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 \rightarrow vehicle means: "The concept car is more specific than the concept vehicle". In order to prune the search space, the rule extraction algorithm takes advantage of the partial order structure provided by the subsumption relation and a property of the implication intensity.

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.

Resources

Aroma development and download site.

License

Aroma is distributed for free, under the LGPL License.