In the vast and diverse landscape of knowledge representation and data modelling, sources of knowledge are articulated through various means, each adopting its distinct methodology for expression. This diversity manifests not only in the choice of technology and representation languages, but also in the vocabularies used and the specific models created. Such heterogeneity, while enriching, introduces significant challenges for semantic interoperability—the ability of different systems to understand and use the information seamlessly across various contexts.

The idea of unifying this rich spectrum of knowledge under a single model and a single representation language, though conceptually appealing, is pragmatically unfeasible and, arguably, undesirable. The diversity of knowledge sources is not merely a by-product of historical development, but a reflection of the varied domains, perspectives, and requirements these sources serve.

To navigate this complexity, a more nuanced approach is required—one that seeks to establish connections across models without imposing uniformity. This is where the concepts of ontology matching and the broader spectrum of model alignment methodologies come into play. Moreover, the matching endeavour encompasses not only ontological artefacts, vocabularies and application profiles, but also various technical artefacts—ranging from data shapes defined in SHACL or ShEx, XSD schemas for XML, to JSON Schemas for JSON data. Each of these artefacts represents a different facet of information modelling and knowledge representation. Thus, mapping in this broader sense involves creating links between these semantic and technical artefacts and Core Vocabularies.

The past couple decades have witnessed extensive efforts in ontology and attendant model matching, resulting in a plethora of tools, methods, and technologies aimed at enhancing semantic interoperability. These endeavours underscore the vast landscape of potential strategies available for matching. These strategies range from conceptual methodologies that explore the semantic congruence and contextual relevance of entities and relationships, to formal methodologies that operationalise these conceptual mappings as technical data transformation rules. It is important to acknowledge that there is no one-size-fits-all method; instead, the field offers a spectrum of approaches suited to various needs and contexts.

The subsequent sections will describe the specific methodologies of matching—both conceptual and formal—and thereby providing a blueprint for navigating and bridging the world of semantic and technical artefacts, empowering stakeholders to make informed decisions that best suit their interoperability needs.