OAEI 2016 results of AML

AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2016 competition and discusses its results. For this OAEI edition, we tackled instance matching for the first time, thus expanding the coverage of AML to all types of ontology matching tasks. We also explored OBO logical definitions to match ontologies for the first time in the OAEI. AML was the top performing system in five tracks (including the Instance and instance-based Process Model tracks) and one of the top performing systems in three others (including the novel Disease and Phenotype track, in which it was one of three prize recipients). 1 Presentation of the System 1.1 State, Purpose, General Statement AgreementMakerLight (AML) is an automated ontology matching system derived from AgreementMaker [3, 4] and designed to tackle large-scale matching problems [6]. It is based primarily on lexical matching techniques, with an emphasis on the use of external resources as background knowledge. This year, our development of AML was focused primarily on tackling instance matching, an aspect of ontology matching that was missing from its portfolio. However, we also made several developments with regard to class matching, namely with the use of OBO logical definitions. For this OAEI edition, we also decided to adopt the solution of using configuration files for each track in order to specify the parameters of the matching task (such as whether to match classes, properties, and/or instances) rather than submit a preconfigured system. With this, we aim at providing a more transparent approach to our participation in the OAEI. 1.2 Specific Techniques Used For the sake of brevity, this section describes only the features of AML that are new for this edition of the OAEI. For a complete description of AML’s matching strategy, please refer to last year’s OAEI results paper [5].