# Modeling and Resolving Uncertainty in DIKWP Model
## 1 Introduction
By mapping key concepts and their interactions, it reveals the possibilities and challenges of compliance pathways;
* Semantic Space (SemA) emphasizes the relationships between semantic units, including the associations and dependencies among vocabulary, regulations, and concepts, thereby ensuring the accurate transmission and interpretation of information and knowledge. Based on the research and discoveries of predecessors [9], driven by purpose, we present in Figure 1 an analytical and processing framework for content(CT) resources across the three spaces mentioned from the perspective of stakeholders. Furthermore, based on the three aforementioned spaces and Figure 1, our definitions for Data, Information, Knowledge, Wisdom, and Purpose are as follows:  
1. **Data (DIKWP-Data)** * **Definition:** Data semantically can be regarded as the concrete manifestation of identical semantics within cognition. This definition emphasizes that in the Cognitive Space, data as cognitive objects are not merely records of facts or observations but are outcomes of cognitive processing that require classification within the Concept or Semantic Spaces. The interpretation involves the cognitive agent's semantic matching and probabilistic confirmation of these data records as cognitive objects. This approach highlights the cognitive attributes of data in communication and thinking, indicating that the significance of data as cognitive objects is realized through specific semantic recognition and conceptual confirmation by the cognitive agent in comparison with existing concepts and semantics. This process underscores the dynamic interaction between the cognitive agent and data, enriching the understanding of how data is assimilated and utilized within cognitive frameworks. In the Concept Space, the concept of data represents the semantic affirmation of specific facts or observations within the Concept Space of a cognitive subject and is validated as the same object or concept by corresponding with some identical semantics included in the consciousness space (not the subconscious space) of the cognitive subject and existing cognitive conceptual objects. In processing the concept of data, the cognitive subject often seeks and extracts specific identical semantics that defines this data concept, thereby unifying them under the same concept based on corresponding identical semantics. For example, when observing a flock of sheep, although each sheep may vary slightly in size, color, and gender, through accurate individual correspondence of identical semantics or probabilistic correspondence of semantic collections, cognitive processing categorizes them under the concept of \"sheep\" because they share a precise or probabilistic correspondence of semantics for the concept of \"sheep\". Identical semantics can be concrete, such as recognizing the concept of an arm by matching the number of fingers, color similarity, and shape between a silicone arm and a human arm, or it can be probabilistically chosen by selecting the target object that shares the most identical semantics with the arm concept. Additionally, the silicone arm's lack of a real arm's rotational functionality, defined by the identical semantics of \"being able to rotate\", can lead to a conceptual veto, thus determining it not to be a data concept of an arm. * **Mathematization of data:** In the DIKWP model, the concept of data is viewed as a concrete manifestation of the same semantics within cognition. Mathematically, the semantic set corresponding to the data concept, denoted as \(D\), can be defined as a vector space where each element \(d\in D\) is a vector representing a specific semantic instance. These semantic instances are grouped under the same semantic attribute \(S\) by sharing one or more semantic features \(F\), defined as \[S=\{f_{1},f_{2},\ldots,f_{n}\}\] (1) where \(f_{i}\) represents a semantic feature of the data concept.