**3. Proposed semantic-based LOD framework (SBLODF)**

The representation (modeling) of knowledge using ontologies (e.g. taxonomies) helps in organizing *metadata* for complex information or data structures. According to Sheth et al. [41], description of real-time processes through metadata creation provides a syntactic as well as semantic way of representing information about the resources that are encoded as instances (entities) in ontological form. Besides, the formal representation of ontologies and the underlying metadata created as a result of the representations allows for automatic reasoning of the processes by making references (inference) to the defined concepts [42]. Indeed, with such reasoning aptitude, the process analysts or owners are able to ensure specification of the process domains (knowledge) in view in an ontological form that can logically be interpreted in an apt way. Consequently, this permits for automatic reasoning of the different concepts to derive an explicit/implicit knowledge about the process domains in question [43].

Therefore, the main benefits of ontologies for formal integration of datasets and models in any shape or platform can be summarized in two forms: (i) encoding knowledge about the specific process domains, and (ii) conceptual analysis and reasoning of the processes at more abstraction levels as described in detail in the following section.

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**Figure 1.**

*Semantic-based linked open data framework (SBLODF).*

or mechanisms.

*Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework*

Information retrieval and structuring of the different sets of data that are stored in several databases or knowledge-base are usually performed in alignment with the users' query [38]. As gathered in **Figure 1**, the supported formats may be a list of document files or keywords issued to the system through the query module (functional operators). In turn, the retrieval module references the properties descriptions (conceptual assertions) that underlie the (semantic) models to produce information that is relevant to the users' query. For example, using the superClass-subClass hierarchies that are usually defined in a taxonomical form in ontologies. This is done through the classification process (e.g. classifying by using a reasoner) to compute the relevant information (e.g. individual entities or process instances) that fulfills the properties restriction by definition [44]. Technically, the most fitting (related) concepts are then presented to the user in a formal way, e.g. explicitly and implicitly. Furthermore, we note that information retrieval and extraction systems such as the SBLODF framework (**Figure 1**) typically do not only support unstructured data or documents (e.g. textual data), but it also deals with semi-structured and structured data. This is where the semantic technologies and such type of systems (which combines the information retrieval (IR) with information extraction (IE) features) [38] becomes greatly beneficial. Functionally, the resulting system allows for merging and manipulation of structured, semi-structured, and unstructured data through the search (query) modules by enabling a conceptual intersection or reasoning between the different elements as contained in the system. Thus, the SBLODF is referred to as a conceptualization method or information processing system that combines the features of the machine-readable and machine-understandable systems

For example, enterprise vendors such as FAST (a Microsoft subsidiary) incorporated analytical search functions to support data visualization and reporting into

*DOI: http://dx.doi.org/10.5772/intechopen.94504*

**3.1 Architecture of the SBLODF framework**
