FOAM - Framework for Ontology Alignment and Mapping - Results of the Ontology Alignment Evaluation Initiative.

Abstract

This paper briefly introduces the system FOAM and its underlying techniques. We then discuss the results returned from the evaluation. They were very promising and at the same time clarifying. Concisely: labels are very important; structure helps in cases where labels do not work; dictionaries may provide additional evidence; ontology management systems need to deal with OWL-Full. The results of this paper will also be very interesting for other participants, showing specific strengths and weaknesses of our approach. 1. PRESENTATION OF THE SYSTEM 1.1 State, purpose, general statement In recent years, we have seen a range of research work on methods proposing alignments [1; 2]. When we tried to apply these methods to some of the real-world scenarios we address in other research contributions [3], we found that existing alignment methods did not suit the given requirements: • high quality results; • efficiency; • optional user-interaction; • flexibility with respect to use cases; • and easy adjusting and parameterizing. We wanted to provide the end-user with a tool taking ontologies as input and returning alignments (with explanations) as output meeting these requirements. 1.2 Specific techniques used We have observed that alignment methods like QOM [4] or PROMPT [2] may be mapped onto a generic alignment process (Figure 1). Here we will only mention the six major steps to clarify the underlying approach for the FOAM tool. We refer to [4] for a detailed description. 1. Feature Engineering, i.e. select excerpts of the overall ontology definition to describe a specific. This includes individual features, e.g. labels, structural features, e.g. subsumption, but also more complex features as used in OWL, e.g. restrictions. 2. Search Step Selection, i.e. choose two entities from the two ontologies to compare (e1,e2). 3. Similarity Assessment, i.e. indicate a similarity for a given description (feature) of two entities (e.g., simsuperConcept(e1,e2)=1.0). 4. Similarity Aggregation, i.e. aggregate the multiple similarity assessments for one pair of entities into a single measure. 5. Interpretation, i.e. use all aggregated numbers, a threshold and an interpretation strategy to propose the alignment (align(e1)=‘ e2’). This may also include a user validation. 6. Iteration, i.e. as the similarity of one alignment influences the similarity of neighboring entity pairs; the equality is propagated through the ontologies. Finally, we receive alignments linking the two ontologies. This general process was extended to meet the mentioned requirements. • High quality results were achieved through a combination of a rule-based approach and a machine learning approach. Underlying individual rules such as, if the super-concepts are similar the entities are similar, have been assigned weights by a machine learnt decision tree [5]. Especially steps 1, 3 and 4 were adjusted for this. Currently, our approach does not make use of additional background knowledge such as dictionaries here. • Efficiency was mainly achieved through an intelligent selection of candidate alignments in 2, the search step selection [4]. • User-interaction allows the user intervening during the interpretation step. By presenting the doubtable alignments (and only these) to the user, overall quality can be considerably increased. Yet this happens in a minimal invasive manner. • The system can automatically set its parameters according to a list of given use cases, such as ontology merging, versioning, ontology mapping, etc. The parameters also change according to the ontologies to align, e.g., big ontologies always require the efficient approach, whereas smaller ones do not [6]. • All these parameters may be set manually. This allows using the implementation for very specific tasks as well. • Finally, FOAM has been implemented in Java and is freely available, thus extensible. 1.3 Adaptations made for the contest No special adjustments have been made for the contest. However, some elements have been deactivated. Due to the small size of the benchmark and directory ontologies efficiency was not used, userinteraction was removed for the initiative, and no specific use case parameters were taken. A general alignment procedure was applied. The system used for the evaluation is a derivative of the ontology alignment tool used in last year’s contests I3Con [7] and EONOAC [8].