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This thesis presents a visual representation approach for Linked Data mappings known as Jigsaw Puzzles for Representing Mappings, or Juma. The term Linked Data refers to a set of best practices for publishing and interlinking data on the Web. A Linked Data dataset is structured information encoded using the Resource Description Framework (RDF), in which resources are identified by and linked with other datasets using HTTP URIs.

Linked Data datasets cover a wide range of knowledge domains, where often concepts overlap. In such cases, mappings can be created to reduce heterogeneity and facilitate the consumption of information by informing agents which concepts are related, and how. These types of mappings are called semantic mappings. Another area in which we find use for mappings is when transforming data from one representation to another–from non-RDF to RDF for example. We call those mappings uplift mappings. Producing such mappings can be difficult, even for experts in Semantic Web technologies, requiring knowledge on the specifics of the mapping language being used as well as significant amount of human effort for their creation, modification, curation and maintenance. Nonetheless, literature suggests that this user involvement is fundamental for producing quality mappings. Suitable visual representations may be used to support user involvement and alleviate the knowledge required for producing Linked Data mappings. Through a systematic literature review, a set of requirements for a visual representation for Linked Data mappings were defined. Juma was then proposed as a novel approach, based on the …