Abstract. Thesaurus alignment plays an important role in realising efficient access to heterogeneous Cultural Heritage data. Current ontology alignment techniques, however, provide only limited value for such access as they consider little if any requirements from realistic use cases or application scenarios. In this paper, we focus on two real-world scenarios in a library context: thesaurus merging and book re-indexing. We identify their particular requirements and describe our approach of deploying and evaluating thesaurus alignment techniques in this context. We have applied our approach for the Ontology Alignment Evaluation Initiative, and report on the performance evaluation of participants ’ tools wrt. the application scenario at hand. It shows that evaluations of tools requires significant effort, but when done carefully, brings many benefits. 1

Abstract. This article describes a base system for ontology alignment, SAMBO, and an extension, SAMBOdtf. We present their results for the benchmark, anatomy and FAO tasks in the 2008 Ontology Alignment Evaluation Initiative. For the benchmark and FAO tasks SAMBO uses a strategy based on string matching as well as the use of a thesaurus. It obtains good results in many cases. For the anatomy task SAMBO uses a combination of string matching and the use of domain knowledge. This combination performed well in former evaluations using other anatomy ontologies. SAMBOdtf uses the same strategies but, in addition, uses an advanced filtering technique that augments recall while maintaining a high precision. 1 Presentation of the system In this section we present the purpose of SAMBO and SAMBOdtf, the framework on which they are built, the specific techniques that are used and the adaptations made for the evaluation. 1 1.1 State, purpose, general statement Although several of our methods and techniques are general and applicable to different areas, when developing SAMBO, we have focused on biomedical ontologies. We chose this field because ontologies are recognized as important in some of the grand challenges in the biomedical domain, and many biomedical ontologies have been developed and are publicly available and have overlapping information. This has, however, had an influence on the approaches on which we focused. In general, ontologies may contain concepts, relations, instances and axioms. Most biomedical ontologies are controlled vocabularies, taxonomies, or thesauri. This means that they may contain concepts, is-a and part-of relations, and sometimes a limited number of other relationships. Therefore, we have mainly developed methods that are based on these ontology components. For some approaches we have also used documents about a concept as instances for that concept. We have not dealt with axioms. SAMBOdtf is an extension of SAMBO that uses an advanced filtering method. 1 Some parts of the description of the system are the same as last year’s description in [11].instance corpus general dictionary domain thesauri alignment algorithm s

A recurring manual task in data integration, ontology alignment or model management is finding mappings between complex meta data structures. In order to reduce the manual effort, many matching algorithms for semi-automatically computing mappings were introduced. Unfortunately, current matching systems severely lack performance when matching large schemas. Recently, some systems tried to tackle the performance problem within individual matching approaches. However, none of them developed solutions on the level of matching processes. In this paper we introduce a novel rewrite-based optimization technique that is generally applicable to different types of matching processes. We introduce filter-based rewrite rules similar to predicate push-down in query optimization. In addition we introduce a modeling tool and recommendation system for rewriting matching processes. Our evaluation on matching large web service message types shows significant performance improvements without losing the quality of automatically computed results.

Abstract. This article describes a system for ontology alignment, SAMBO, and presents its results for the benchmark and anatomy tasks in the 2007 Ontology Alignment Evaluation Initiative. For the benchmark task we have used a strategy based on string matching as well as the use of a thesaurus, and obtained good results in many cases. For the anatomy task we have used a combination of string matching and the use of domain knowledge. This combination performed well in former evaluations using other anatomy ontologies. 1

Ontology matching aims to find semantic correspondences between a pair of input ontologies. A number of matching techniques have been proposed recently, however, we may benefit more from a combination of such techniques as opposed to just a single method. This is more appropriate, but very often the user has no prior knowledge about which technique is more suitable for the task at hand. However, it remains a labour intensive and expensive task to perform. Further, the complexity of the matching process as well as the quality of the result is affected by the choice of the applied matching techniques. We study this problem and propose a framework for finding suitable matches. A main feature of this is that it improves the structure matching techniques and the end result accordingly. We have developed a running prototype of the proposed framework and conducted experiments to compare our results with existing techniques. While being comparable in efficiency, the experimental results indicate our proposed technique produces better quality matches.

for argumentation on mappings

Ontology matching takes ontologies as input and determines as output correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, query answering, data translation, or for navigation on the semantic web. Ontology

In recent years many ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned. In this paper we present and discuss results from aligning ontologies in a real case, the anatomy case in the 2008 Ontology Alignment Evaluation Initiative. We do this by briefly describing a base system for ontology alignment, SAMBO, and an extension, SAMBOdtf, and present and discuss their results for the anatomy case. SAMBO and SAMBOdtf performed best and second best among the 9 participating systems. SAMBO uses a combination of string matching and the use of domain knowledge. SAMBOdtf uses the same strategies but, in addition, uses an advanced filtering technique that augments recall while maintaining a high precision. Further, we describe the first results on ontology alignment using a partial reference alignment. 1 1

In this abstract we tackle the problem of aligning large ontologies where the mappings suggested by the ontology alignment system need to be validated. Although we focus on an ontology alignment framework, the ideas may be used and extended for community-based or collaborative ontology alignment. In contrast to the case of small ontologies, the computation of mapping suggestions can take a long time and therefore, we would like to be able to start the validation before every mapping suggestion is computed. Further, it is clear that for large ontologies, in general, there are too many mapping suggestions to validate in one time. Therefore, we want a system that allows to partially validate the mapping suggestions and resume the validation later. However, whenever validation decisions have been made, they increase our knowledge about the ontologies and mappings and this knowledge can be used to provide better mapping suggestions. In the remainder of the abstract we propose an iterative ontology alignment framework that deals with these issues. Framework. Our framework is presented in figure 1. The input to the system are the ontologies that need to be aligned, and the output is an alignment between the ontologies.