No abstract

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.

Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, alignments produced for large ontologies are often logically incoherent. It was only recently that the use of repair techniques to improve the coherence of ontology alignments began to be explored. This paper presents a novel modularization technique for ontology alignment repair which extracts fragments of the input ontologies that only contain the necessary classes and relations to resolve all detectable incoherences. The paper presents also an alignment repair algorithm that uses a global repair strategy to minimize both the degree of incoherence and the number of mappings removed from the alignment, while overcoming the scalability problem by employing the proposed modularization technique. Our evaluation shows that our modularization technique produces significantly small fragments of the ontologies and that our repair algorithm produces more complete alignments than other current alignment repair systems, while obtaining an equivalent degree of incoherence. Additionally, we also present a variant of our repair algorithm that makes use of the confidence values of the mappings to improve alignment repair. Our repair algorithm was implemented as part of AgreementMakerLight, a free and open-source ontology matching system.

Abstract. BioPortal is a repository for biomedical ontologies that also includes mappings between them from various sources. Considered as a whole, these mappings may cause logical errors, due to incompatibilities between the ontologies or even erroneous mappings. We have performed an automatic evaluation of BioPortal mappings between 19 ontology pairs using the mapping repair systems of LogMap and AgreementMakerLight. We found logical errors in 11 of these pairs, which on average involved 22% of the mappings between each pair. Furthermore, we conducted a manual evaluation of the repair results to identify the actual sources of error, verifying that erroneous mappings were behind over 60% of the repairs. Given the results of our analysis, we believe that annotating BioPortal mappings with information about their logical conflicts with other mappings would improve their usability for semantic web applications and facilitate the identification of erroneous mappings. In future work, we aim to collaborate with BioPortal developers in extending BioPortal with these annotations. Despite some community efforts to ensure a coordinated development of biomedical ontologies [38], many ontologies are being developed independently by different groups of experts and, as a result, they often cover the same or related subjects, but follow different modeling principles and use different entity naming schemes. Thus, to integrate data among applications, it is crucial to establish correspondences (called mappings) between the entities of the ontologies they use.In the last ten years, the semantic web and bioinformatics research communities have extensively investigated the problem of (semi-)automatically computing correspondences between independently developed ontologies, which is usually referred to as the ontology matching problem. Resulting from this effort are the growing number of ontology matching systems in development [8,7,37] and the large mapping repositories that have been created (e.g., [2,10]).

To bring the Life Sciences domain closer to a Semantic Web realization it is fundamental to establish meaningful relations between biomedical ontologies. The successful application of ontology matching techniques is strongly tied to an effective exploration of the complex and diverse biomedical terminology contained in biomedical ontologies. In this paper, we present an overview of the lexical components of several biomedical ontologies and investigate how different approaches for their use can impact the performance of ontology matching techniques. We propose novel approaches for exploring the different types of synonyms encoded by the ontologies and for extending them based both on internal synonym derivation and on external ontologies. We evaluate our approaches using AgreementMaker, a successful ontology matching platform that implements several lexical matchers, and apply them to a set of four benchmark biomedical ontology matching tasks. Our results demonstrate the impact that an adequate consideration of ontology synonyms can have on matching performance, and validate our novel approach for combining internal and external synonym sources as a competitive and in many cases improved solution for biomedical ontology matching.