**1. Introduction**

Linked Open Data (LOD) is a term used to refer to tools or platforms that support freely-connected (interlinked) resources or frameworks to allow for collection and integration of data (usually derived from various sources or formats) and provide useful information that can be accessed by machines or humans. Typically, LOD supported tools or platforms is expected to allow for both simple or complex oriented lookup for information access through some form of predefined language or mechanisms (e.g. using scripts or query-based languages such SQL, HTML, SPARQL, Description Logics, RDF graphs of the Triples form, XML, etc.) [1–3]. Technically, LOD is semantically defined as a knowledge graph [3] that vents in the form of

semantical web or schema (e.g. using ontologies) [4–6] of interconnected data [7]. According to Snyder et al. [7], LOD has since been epitomized as a way of improving the process of discovering useful information or resources by creating a series of robust links between related concepts or items.

The work done in this chapter notes that one of the main challenges with LOD has been on how to create systems or methods that are capable of providing an understandable format (both machine-readable and machine-understandable) for the various datasets that may come from different sources, as well as, making the derived formats or standards explicable across the several platforms. To this end, the work proposes a semantic-based LOD framework (SBLODF) that provides an additional function to LOD that allows for formal integration of the process elements or concepts through metadata creation (process description) using the semantic technologies or schema. This is called Semantic-based Linked Open Data.
