*Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework DOI: http://dx.doi.org/10.5772/intechopen.94504*

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., what is true and not fitting in the model.

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

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 in the model.

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


**Figure 3.** *Example of object property description and assertion for the true trace classification.*

*Linked Open Data - Applications, Trends and Future Developments*

(CLMCU) incremental or semantic modeling procedure [6, 11].

extraction (OBIE) [9].

(machine-understandable).

**5. Data analysis and implementation results**

Interestingly, Dou et al. [8] note that a well-designed information retrieval or data/process mining system should present the outcomes or discovered information in a formal and structured format qua being interpreted as domain knowledge, or yet, utilized to further augment the existing system. Besides, the work [8] states that ontological schema is one of the most effective ways to formally represent any given type of data or process models. This is due to the fact that concepts defined within ontologies can be expressed or represented as set(s) of annotated terms and/or relations that aims to support information extraction and association rule mining systems especially with those allied to the ontology-based information and

To this effect, this current study note that to implement the aforementioned functionalities of the ontology-based systems in the SBLODF framework, the extracted information or models from the standard process mining (management) or analysis tools/sources needs to be represented as sets of annotated terms (that links or connects the defined terms) in an ontological form using the create-link-map-check-use

As illustrated in **Figure 2**, the resultant class hierarchies or taxonomy (ontologies) tends to provide a way of formally representing the defined (annotated) terms or concepts in a structured format by ascertaining the relationships (association) that co-exist amongst the several entities within the process model. Henceforth, the process descriptions and assertions are realized by encoding the process model in the formal structure or taxonomy, thus far ontologies, for the information/ knowledge extraction to follow. In the end, the system is integrated or manipulated with an inference engine (e.g. reasoner or classifier) that performs semantic reasoning by uncovering the different levels of the ontological classification and process elements to produce the (inferred) information (knowledge) based on the input queries or users search that displays to be closer to human understanding

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

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

**48**

Logic (DL) [2].


**51**

True negatives (TN):

False negatives (FN)

No. of traces correctly classified

**Table 1.** *Classification results and performance of the discovered models.*

20

20 *Note: cells with gold sign (\*) indicates traces that were correctly classified by the reasoner which equals to 200 traces out of 200.*

20

20

20

20

20

20

20

20

**Model 1**

10 0

0

0

0

0

0

0

0

0

0

10

10

10

10

10

10

10

10

10

**Model 2**

**Model 3**

**Model 4**

**Model 5**

**Model 6**

**Model 7**

**Model 8**

**Model 9**

**Model 10**

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

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


*Linked Open Data - Applications, Trends and Future Developments*

**50**

Trace\_1 Trace\_2 Trace\_3 Trace\_4 Trace\_5 Trace\_6 Trace\_7 Trace\_8 Trace\_9 Trace\_10 Trace\_11 Trace\_12 Trace\_13 Trace\_14 Trace\_15 Trace\_16 Trace\_17 Trace\_18 Trace\_19 Trace\_20 True positives (TP):

False positives (FP):

**Model 1**

TP \* TN \* TP \* TP \* TN \* TP \* TN \* TN \* TP \* TP \* TN \* TP \* TP \* TN \* TP \* TN \* TP \* TN \* TN \* TN \*

10 0

0

0

0

0

0

0

0

0

0

10

10

10

10

10

10

10

10

10

TN \*

TN \*

TN \*

TP \*

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TP \*

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TP \*

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TN \*

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TP \*

TP \*

TP \*

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TN \*

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TP \*

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TN \*

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TP \*

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TP \*

**Model 2**

**Model 3**

**Model 4**

**Model 5**

**Model 6**

**Model 7**

**Model 8**

**Model 9**

**Model 10**
