**5. Data analysis and implementation results**

For the data analysis and implementation in this section of the chapter; the work uses dataset about a real-time business process provided by the IEEE CIS Task Force on Process Mining [51] to illustrate how the proposed method is capable of performing the information retrieval and extraction process by integrating the different components of the SBLODF framework, as described in **Figure 1**. Typically, this is done by enabling a conceptual intersection or reasoning between the different elements/components which are supported by the system. These functions ranges from the user input query or search module to the information retrieval module or input reader (machine-readable component), and then, the metadata descriptions/assertions, ontological modeling and class hierarchies (taxonomy) to the provision of formal knowledge (explicit and implicit information) that can be easily understood by humans in real-world settings. Fundamentally, the work note the key function of the SBLODF framework to be in its capability to utilize the semantic concepts to perform automatic (semantic) reasoning/inferences capable of discovering useful models and conceptual information from the dataset. Henceforth, the SBLODF implementation allows the meaning of the process elements to be enhanced through the use of property description languages and classification of the discoverable entities, for example, using the Web Ontology Language (OWL) [4], Semantic Web Rule Language (SWRL) [52], and Description Logic (DL) [2].

Practically, as shown earlier in **Figure 2**, the ontological schema or framework trails to connect the different sets of discoverable entities in the model with their

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**Figure 3.**

in the model.

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

Classification\_(TP)" or "FalseTrace\_Classification\_(TN)".

*Example of object property description and assertion for the true trace classification.*

class membership, or yet with a fixed literal, and can also describe the sub assumption hierarchies (taxonomies) that exists between the various classes including the relationships that they share within the underlying model. Moreover, the different class(es) are consequently instantiated with the set of individuals, *I*, and can also contain the various set of axioms, *A*, which states facts. For instance, the true positive elements, i.e., what is true and fitting within the model, and true negatives, i.e.,

To illustrate this, the work analyzes the data provided in Ref. [51], by making use of the object properties (see: **Figure 2**) to describe the different classes that can be found within the semantic model developed with Protégé Editor for the purpose of this work. As shown in **Figure 3**, it used the "hasTraceFitness" object property to describe the classes or entities in the test data log that has a "TrueTrace\_

Moreover, as defined in Section 2.2 and Section 4 (**Figure 2**), if we Let *A*, be the set of all process executions or actions that can be performed within the semantic model. A process action *a* ∈ *A* is characterized by a set of input parameters *Ina* ∈ *P* which is required for the execution of *a*, and a set of output parameters *Outa* ⊆ *P* which is produced by *a* after the execution or search query. Thus, with such function, the extraction and automatic reasoning (e.g. classification) of the process parameters is enabled and/or supported by the model. Perhaps, the key purpose of implementing the framework is to match the questions one would like to answer about attributes/relationships the process instances share amongst themselves within the knowledge-base by linking to the concepts (inferred classes) described

As shown in **Table 1**, based on the features of the provided datasets [51], the work applies the cross-validation technique to analyze the training and test sets. The traces were computed and recorded according to the *reasoner* response, and the classifier (reasoner) was tested on the resulting individuals by assessing its performance with respect to the correctly classified traces. As an example, the following DL queries/syntax [2] represents as set of input parameters (search query) the work executed in order to output the set of traces that can be found within the

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

what is true and not fitting in the model.
