In recent years, Linked (Open) Data has emerged as a prominent framework for publishing structured data on the Web adopted by various domains including geosciences. Linked Data allows data from different sources to be interlinked using HTTP Uniform Resource Identifiers (URIs) and be machine-processable in a standard way via the Resource Description Framework (RDF). Interoperability and integration across different datasets are achieved by the use of vocabulary that is agreed upon by the community or standardized by some governance body. Such a vocabulary is often specified in an ontology, which formalizes the semantics of the vocabulary terms being used. The challenge is that many ontologies, including domain ontologies, are too complicated, restrictive, and difficult to use and understand. This makes many linked data publishers avoid ontologies and prefer to simply use less formal vocabulary. Although this allows linked data publishing staying relatively simple, the resulting datasets would only have a low quality metadata, making the datasets harder to understand, interoperate, and integrate. In this tutorial lecture, we shall introduce a modular ontology architecture based on the so-called ontology design patterns, which are sufficiently flexible, easier to understand, and less restrictive, while allowing the linked datasets to be equipped with a sufficiently high quality metadata, enabling interoperability and easier integration across semantically heterogeneous datasets. We will demonstrate how such an ontology architecture works in a data integration setting, catering multiple perspectives from different data providers, as well as accommodating existing vocabulary that are already employed by the community.
