AgreementMakerLight results for OAEI 2014

AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2014 competition and discusses its results. Our goal this year was broadening the scope of AML by delving into aspects such as translation and structural matching, while reinforcing the key aspects behind its success last year (i.e., element-level matching, the use of background knowledge, and alignment repair). AML’s participation in the OAEI 2014 was very successful, as it obtained the highest F-measure in 6 of the 8 ontology matching tracks. 1 Presentation of the system 1.1 State, purpose, general statement AgreementMakerLight (AML) is an automated ontology matching system derived from AgreementMaker [1, 2] and developed to handle large ontology matching problems. It combines the design principles of AgreementMaker (flexibility and extensibility) with a strong focus on efficiency [6]. Furthermore, it draws on the knowledge accumulated in AgreementMaker by reusing and adapting several of its components, but also includes a growing number of novel components. AML is primarily based on lexical matching techniques, with an emphasis on the use of external resources as background knowledge. It also emphasizes alignment coherence, featuring an improved alignment repair module. While initially AML was mainly focused on the biomedical domain, we have striven to expand its scope throughout the last year, and it is now a general-purpose ontology matching system. We have also moved towards full automation by employing a general-purpose core matching strategy complemented with an automated background knowledge selection algorithm [5]. 1.2 Specific techniques used The AML workflow for the OAEI 2014 comprises nine different steps, as shown in Figure 1: ontology loading and profiling, translation, baseline matching, background knowledge matching, word and string matching, structural matching , property matching, selection, and repair. The key differences from last year’s workflow are the introduction of the translation and structural matching steps.