**4. Implementation components of the semantic-based linked open data framework (SBLODF)**

The work describes in this section (**Figure 2**) how the semantic schema is used to support the development of the LOD framework. Ontology-based information retrieval and extraction systems such as the SBLODF (**Figure 1**) are construed on the main building blocks [31]:


## **Figure 2.**

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

information (data) access and utilization.

(tailored content) information to the users.

data items.

their products [38, 45]. Moreover, Ingvaldsen [38] notes that the business process intelligence (BPI) solutions and offerings can also benefit from such a combination (IR and IE supported systems) by giving the users a search facilitated (data analysis) environment to harvest/harness data from both structured and unstructured data sources. Thus far, giving the users a more flexible environment for accessing relevant

Interestingly, semantic-based information retrieval and extraction systems as illustrated in **Figure 1**, represents to be a step further in supporting the BPI's by providing additional modules or components that allows for integrating metadata description (e.g. ontologies) to the system design or framework. The semantic-based components (see: **Figure 1**) aims to add a machine tractable and/or re-purposeable layer of annotations that are relative to ontologies in order to complement the existing web of information and data analysis procedures, or yet, the omnipresence of natural language hypertext [4, 46, 47]. Perhaps, this is fundamentally done through the creation of semantic annotations [11, 23] and linking of the different concepts or modules to ontologies. In turn, the semantically motivated process or models turns out to become automatic or semi-automatic in nature and allows for ample integration of the LOD frameworks due to creation, interrelation, or application of the ontologies (semantic schema). Besides, this has led to the advancement of hybrid intelligent systems such as the ontology-based information extraction systems (OBIE) [9, 13, 15]. Explaining why IE and semantic technologies can be used to bring together a common language or syntax upon which the LOD systems or web search are built specially given the ever-needed formal knowledge or tools for

Some examples of state-of-the-art tools or systems that trails to support the semantic-based LOD framework or search include; KIM (knowledge and information management system) [31, 48] an extendable platform for information management that seemingly offers IE-based functions for metadata creation and search. Technically, KIM consists of a set of front-end (user-interface) for online information search by offering semantically-enhanced browsing features. Another tool that tends to support the semantic-based LOD, such as the SBLODF framework described in this chapter, is Magpie [49]. Magpie is developed and implemented as an add-on to web browsers by using IE mechanisms to support collaborative information interpretation and modeling of the extracted knowledge from the web. As illustrated in **Figure 1**, it annotates the different web pages with metadata descriptions in an automated manner by automatically populating ontologies from the relevant (web) sources. Thus, the application (Magpie) is interoperable with ontologies or semantic schema. Moreover, it is important to mention that one of the fundamental elements of the tool (Magpie) that is pertinent to this work is the fact that it makes use of ontologies to provide specific

There are several other platforms that can be referred to also support the SBLODF framework. This includes the SemTag [50] which utilizes IE facilities or function to support large scale semantic annotations and process descriptions using TAP ontology. As described in **Figure 1**, SemTag functions by performing annotation of all defined mentions (references) of any given process instance or entity in the ontology (TAP) through a lookup phase. This lookup process is then followed by the disambiguation phase during which it assigns the right classes (or establishes instances that

do not correspond to a class in the TAP) using a vector-space model [50].

**46**

*Implementing the semantics components in SBLODF using create-link-map-check-use (CLMCU) procedure [11].*

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 extraction (OBIE) [9].

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

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 (machine-understandable).
