OntologieAlignment

OntologieAlignment, more commonly referred to as ontology alignment or ontology matching, is the process of identifying and establishing correspondences between the entities of different ontologies. The aim is to enable interoperability, data integration, and unified reasoning across heterogeneous knowledge sources. An alignment typically results in a set of mappings between ontology elements such as classes, properties, and individuals, which can be used to translate queries, merge schemas, or drive cross-ontology reasoning.

Entities involved in alignment include classes, properties, and individuals from each ontology. Correspondences may express various relations, with equivalence (two entities denote the same concept), subsumption (one concept subsumes another), and disjointness among the common types. Alignments are often serialized in formats compatible with the OWL and RDF ecosystems and may be represented using the Alignment Format or through the Alignment API, sometimes accompanied by confidence scores reflecting the strength of each mapping.

Methods for finding alignments combine lexical, structural, and semantic techniques. Approaches include string similarity for labels, structural matching based on hierarchy and property structure, instance-based methods using data values, and semantic methods leveraging external knowledge bases. More recently, machine learning and embedding-based techniques have been explored to improve accuracy, particularly at scale. Human validation and iterative refinement are common to ensure coherence and usefulness.

Evaluation typically uses metrics such as precision, recall, and F-measure, often within standardized benchmarks from the Ontology Alignment Evaluation Initiative (OAEI). Tools in the field include LogMap, AgreementMaker, AML, COMA++, and MAF, which implement various combinations of the methods above. Ontology alignment underpins many applications in data integration, semantic interoperability, and cross-domain information retrieval.