Results of the ontology alignment evaluation initiative 2019

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign

Complex correspondences for query patterns rewriting

International audienceThis paper discusses the use of complex alignments in the task of automatic query patterns rewriting. We apply this approach in SWIP, a system that allows for querying RDF data from natural language-based queries, hiding the complexity of SPARQL. SWIP is based on the use of query patterns that characterise families of queries and that are instantiated with respect to the initial user query expressed in natural language. However, these patterns are specific to the vocabulary used to describe the data source to be queried. For rewriting query patterns, we experiment ontology matching approaches in order to find complex correspondences between two ontologies describing data sources. From the alignments and initial query patterns, we rewrite these patterns in order to be able to query the data described using the target ontology. These experiments have been carried out on an ontology on the music domain and DBpedia ontology

Milan: automatic generation of R2RML mappings

Milan automatically generates R2RML mappings between a source relational database and a target ontology, using a novel multi-level algorithms. It address real world inter-model semantic gap by resolving naming conflicts, structural and semantic heterogeneity, thus enabling high fidelity mapping generation for realistic databases. Despite the importance of mappings for interoperability across relational databases and ontologies, a labour and expertise-intensive task, the current state of the art has achieved only limited automation. The paper describes an experimental evaluation of Milan with respect to the state of the art systems using the RODI benchmarking tool which shows that Milan outperforms all systems in all categorie

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology

Génération automatique d'alignements complexes d'ontologies

Le web de données liées (LOD) est composé de nombreux entrepôts de données. Ces données sont décrites par différents vocabulaires (ou ontologies). Chaque ontologie a une terminologie et une modélisation propre ce qui les rend hétérogènes. Pour lier et rendre les données du web de données liées interopérables, les alignements d'ontologies établissent des correspondances entre les entités desdites ontologies. Il existe de nombreux systèmes d'alignement qui génèrent des correspondances simples, i.e., ils lient une entité à une autre entité. Toutefois, pour surmonter l'hétérogénéité des ontologies, des correspondances plus expressives sont parfois nécessaires. Trouver ce genre de correspondances est un travail fastidieux qu'il convient d'automatiser. Dans le cadre de cette thèse, une approche d'alignement complexe basée sur des besoins utilisateurs et des instances communes est proposée. Le domaine des alignements complexes est relativement récent et peu de travaux adressent la problématique de leur évaluation. Pour pallier ce manque, un système d'évaluation automatique basé sur de la comparaison d'instances est proposé. Ce système est complété par un jeu de données artificiel sur le domaine des conférences.The Linked Open Data (LOD) cloud is composed of data repositories. The data in the repositories are described by vocabularies also called ontologies. Each ontology has its own terminology and model. This leads to heterogeneity between them. To make the ontologies and the data they describe interoperable, ontology alignments establish correspondences, or links between their entities. There are many ontology matching systems which generate simple alignments, i.e., they link an entity to another. However, to overcome the ontology heterogeneity, more expressive correspondences are sometimes needed. Finding this kind of correspondence is a fastidious task that can be automated. In this thesis, an automatic complex matching approach based on a user's knowledge needs and common instances is proposed. The complex alignment field is still growing and little work address the evaluation of such alignments. To palliate this lack, we propose an automatic complex alignment evaluation system. This system is based on instances. A famous alignment evaluation dataset has been extended for this evaluation