科学网-Data Definition and Processing for the DIKWP Model-段玉聪的博文

Professor Yucong Duan proposed the definition of data (DIKWP-Data) in the DIKWP model, emphasizing the specific manifestations of data expressing the same meaning in the cognitive process. This definition differs significantly from traditional DIKW models, semantic web and ontology, cognitive science, and data definitions in artificial intelligence. This article compares and analyzes the definitions of data in various models in detail, exploring their differences in semantic matching, concept confirmation, subjectivity, and contextual dependency. The aim is to deepen the understanding of data processing and cognitive processes, and provide a theoretical basis for research in related fields.

Introduction

In the data-driven era, the definition and processing methods of data are crucial for the fields of information science, cognitive science, and artificial intelligence. The traditional DIKW model views data as unprocessed facts and observations, emphasizing that data is the foundation of information, knowledge, and wisdom. The semantic web and ontology achieve interoperability and standardization of data by defining semantic labels for the data. Cognitive science focuses on the storage and processing of data in the brain, while artificial intelligence uses data as the fundamental entity for training models and reasoning.

Professor Yucong Duan proposed a new data definition in the DIKWP model, emphasizing that data expresses "the same" meaning through semantic matching and probability confirmation in the cognitive process. This definition endows data with higher subjectivity and contextual dependency, highlighting the semantic processing of data within cognitive subjects. This article aims to provide a detailed comparative analysis of the data definitions in the DIKWP model and other traditional models, and explore its applications and significance in semantic processing, concept confirmation, and cognitive processes.

1 Data definition in professor Yucong Duan's DIKWP model

In the DIKWP model, the semantics of data are the specific manifestations of the semantics that express the same meaning in the cognitive process. This definition emphasizes that cognitive object data in the cognitive space is not only a record of facts or observations as a cognitive result of this cognitive space, but also requires classification and correspondence of the data in conceptual or semantic space, and semantic matching and probability confirmation of the cognitive objects corresponding to these data records by the cognitive subject. This processing method highlights the cognitive attributes of data in communication and thinking, that is, the meaning of the data after completing the cognitive object is identified and conceptually confirmed through the comparison between the cognitive subject and existing concepts and semantics.

The semantics of DIKWP-Data can be seen as a specific manifestation of the same semantics in cognition. In the conceptual space, a data concept, as a concept, represents the semantic confirmation of the existence of specific facts or observations in the conceptual space of the cognitive subject, and is confirmed as the same object or concept by corresponding to certain identical semantics contained in the cognitive subject's conscious space (non subconscious space) and the existence of existing cognitive concept objects. When dealing with data concepts, the cognitive processing of cognitive subjects often seeks and extracts specific identical semantics that calibrate the data concept, and then unifies them as one identical concept based on the corresponding identical semantics. For example, when seeing a group of sheep, although each sheep may have slight differences in body size, color, gender, etc., cognitive processing will classify them under the concept of "sheep" with the help of accurate semantic individual correspondences or probabilistic correspondences to the same semantic set, because they share the precise semantic or probabilistic correspondences to the concept of "sheep". The same semantics can be specific. For example, when recognizing the concept of an arm, accurate semantic confirmation of the silicone arm as an arm concept can be based on the same number of fingers, color, and shape of the arm as a human arm. The target object that shares the most common semantics with the arm concept can also be selected probabilistically as the arm concept. The concept judgment can be rejected based on the same semantics defined by "rotatable" that correspond to the fact that the silicone arm does not have the ability to rotate a real arm, and it can be determined that it is not an arm data concept.

The distinction between conceptual space and semantic space corresponds to different philosophical ideas of technology. The processing of conceptual space corresponds to the specific use of natural language and other forms of communication. But the essential function of conceptual communication is usually to convey semantics. In the cognitive space of the cognitive subject, the effective understanding of the semantics conveyed by concepts often depends on the semantic correspondence of relevant concepts in the cognitive subject's semantic space. The semantic space of cognitive subjects is often not fully shared through conceptual forms, which is commonly referred to as objective, hence it is called subjective.

In the semantic space, the semantics of data concepts are specific manifestations of the same semantic set in cognition. Correspond the semantic D of specific data to a set, where each element d∈D represents a specific instance that shares the same or approximately the same set of semantic attributes S. The semantic attribute S is defined as a set of feature semantic sets F, namely:

S={f₁, f₂, ... , f_n}，Where f+ represents a feature semantic of the data. D={d∣d share S}。

In the DIKWP model, the distinction between data concepts and data semantics is the basis for the transformation of the cognitive process from cognitive space to the processing process of conceptual space and semantic space. Data concepts and data semantics will specifically represent the cognitive data objects of basic observations and facts about the world. The key to this transformation lies in the "same semantics" behind the recognition and conceptualization of data, namely the semantic attributes shared between data concept elements. In cognitive space, data cognitive objects serve as the basis for cognitive processes, and are no longer directly representing the results of observing and measuring the real world without distinguishing between specific correspondences in conceptual and semantic spaces. Instead, they are subjected to clear conceptual confirmation and semantic correspondence processing, which also distinguishes the categories of subjective and objective content. This definition is different from the rough understanding of data in traditional DIKW models, emphasizing the close relationship between data and specific semantic attributes. The cognition of data objects is not only a passive recording, but also a subjective process in which the cognitive subject actively seeks semantic features that match known cognitive objects in their cognitive activities. This perspective emphasizes the subjectivity and contextual dependence of data, pointing out that the cognitive value of data lies in its ability to semantically associate with the existing conceptual space of cognitive subjects.

In the DIKWP model, the semantics of cognitive objects from cognitive space are considered as specific manifestations of the same semantics recognized in the cognitive subject's semantic space during the cognitive process. This definition emphasizes that data processing cognitive objects is not just a simple record of observations or facts, but also the result of semantic matching and conceptual confirmation of these cognitive object data records through cognitive subjects (such as humans or AI systems) in conceptual and semantic spaces. The key to confirming data concepts lies in the shared "same semantics" between the cognitive subject's conceptual space and semantic space behind them, which enables specific cognitive objects to be summarized as the same data concept even in the presence of differences in external features.

Professor Yucong Duan proposed that data concepts are regarded as the basic conceptual units in the cognitive process in the conceptual space of the DIKWP model, and data semantics are regarded as the basic semantic units in the cognitive process in the semantic space of the DIKWP model. Data concepts and data semantics are the core elements in the cognitive process of directly observing and recording the real world. From the recognition of data semantics to the confirmation of data concepts, they play an important role in the generation, application, and processing of concept-based symbolic natural language. The concept of data is recognized and classified by the conscious or subconscious functions of cognitive subjects by sharing the same semantic attributes. In cognitive science, how the cognitive subject's brain and even spinal cord understand and process information through subconscious pattern recognition, and can be consciously analyzed and interpreted. For example, when people observe different objects (such as apples), even if they have different colors, sizes, or shapes, they can still recognize them as apples in subconscious pattern recognition. Through conscious analysis by cognitive subjects, it can be explained that they share a set of key semantic attributes (such as shape, texture, specific functions, etc.). This cognitive process reveals how the cognitive system of the cognitive subject utilizes the same semantics of semantic space data to construct a natural language concept representation of the world.

In the DIKWP framework, data is viewed as a concrete conceptual mapping of the same semantics in cognitive processes. This viewpoint breaks through the traditional confusion of semantic and conceptual usage scenarios in data concepts, and closely links the formation and existence of data concepts in conceptual space with the semantic processing process of cognitive subjects in semantic space. That is, the cognitive value of data concepts does not lie in their corresponding physical form or function, but in how they cross the "conceptual space" and "semantic space" of the cognitive subject's existing knowledge system in the cognitive space, and are then recognized and recognized as objects or concepts with specific semantics. From the perspective of individual consciousness and group consciousness interaction, the interaction between data and cognitive subjects is essentially based on the interaction between subconscious or subconscious semantic space and conceptual space. Data concepts, as specific correspondences or probabilistic approximations of the same semantic set, have cognitive communication efficiency advantages in engineering sense as symbolic expressions of specific semantic sets.

The mathematical representation of data concepts: In the DIKWP model, data concepts are not only passively recorded observation results, but also a set of semantic objects actively recognized and classified by the cognitive system. Mathematically, we can consider data concepts as a set D of semantic instances, where each semantic instance d∈D is recognized as having the same semantic attribute set S. Here, S={f₁, f₂,..., f_n} can be regarded as a set of parameters that define the semantic features of data concepts, where f_i represents a semantic feature of data concepts. This expression helps us understand how data concepts are induced and processed based on shared semantic features.

Mathematical description of data: In the DIKWP model, data concepts are seen as concrete manifestations of the same semantics in cognition. Mathematically, we can define the semantic set D corresponding to data concepts as a vector space, where each element d∈D is a vector representing a specific semantic instance. These semantic instances belong to the same semantic attribute S by sharing one or more semantic features F, namely:

S={f₁,f₂,...,f_n}

Where f_i represents a semantic feature of the data concept. So, we can define the set of data concepts as:

D={d∣d share S}

This description emphasizes the semantic multidimensional and structural nature of data concepts, while providing a mathematical foundation for subsequent data concept processing and analysis.

Data Concept and Semantic Recognition: In the DIKWP model, the processing and understanding of data concepts is not only about recording objective facts, but also involves how cognitive subjects match the semantics of these facts with existing semantic cognitive structures. This process emphasizes the importance of semantic recognition, that is, how cognitive subjects recognize and classify objects through semantic features in data concepts.

The specific manifestations of data concepts and the same semantics: In the DIKWP model, data concepts are not only observations and records of the real world, but are seen as the specific manifestations of cognitive subjects towards the same semantic attributes in communication. This definition goes beyond the surface independent objective cognitive existence of data concepts as records of objective facts, emphasizing the cognitive nature of data concepts in the interaction between cognitive subjects and objects in cognitive space - that is, the recognition and processing of data concepts depend on the connection and matching of existing semantics in the cognitive subject's (subjective) semantic space. The concept of data essentially has cognitive subjectivity and contextual dependence, that is, the relativity of establishing connections and corresponding cognitive processing with different semantics for the same data concept under different cognitive subjects or backgrounds.

Furthermore, in a philosophical sense, the concept of data is no longer an objective factual record, but a subjective interpretation through subjective cognitive processes. The formation and existence of data concepts depend on the cognitive subject's semantic space and conceptual space memory and processing ability. It is the form of association and transformation between the semantic space and conceptual space of the interaction between the real world and cognitive subjects. The generation and recognition of data concepts is not a purely objective process, but deeply rooted in the subject's preset conceptual space and contextual semantic space. Therefore, the recognition and interpretation of data concepts need to take into account the cognitive spatial background knowledge, experiential information, and cultural contextual semantics of the cognitive subject.

The meaning of data concepts must be confirmed through the interpretation and semantic matching of cognitive subjects. The interaction between data concepts and data semantics has become a bridge connecting objective reality and subject cognition. This understanding highlights the Platonic idea that things in the real world (as concepts) are only shadows of their ideas (i.e., "the same semantics"). Therefore, the cognitive value of data concepts lies not only in the objectivity of their manifestations, but also in how cognitive subjects seek and confirm the common semantics of cognitive objects and phenomena through data concepts, triggering semantic resonance and cognitive confirmation. The interaction process between this data concept and data semantics within the cognitive subject is not only a cognitive mirror reflection of the external world of the cognitive subject, but also a pursuit and revelation of the intrinsic semantics of phenomena. It emphasizes the dominance of conceptual semantic transformation and the creativity of conceptual existence in the process of data concept interpretation by cognitive subjects, as well as the interaction between data concepts and cognitive subjects through subconscious or conscious symbolic language.

The cognitive properties and semantic entities of data: The DIKWP model emphasizes the cognitive definition of data concepts and semantics, emphasizing the cognitive properties of data and its role as semantic entities. In philosophy, this touches upon the discussion of the essence of things and their true nature. Data concepts are not just objectively existing symbolic records, they are entities endowed with specific data semantics, which are confirmed and assigned through the processing of cognitive subjects across conceptual and semantic spaces. This cognitive processing also reveals that knowledge generation is not only a mapping of the objective world, but also a subjective process of transformation and construction based on similar semantics to concepts. This is reflected in Kant's prior philosophy, that is, people's knowledge of the world is partly derived from external stimuli, but more determined by our cognitive structure.

2 Overview of the DIKWP model

The DIKWP model divides data, information, knowledge, wisdom, and purpose into five categories, representing the cognitive and processing processes of different categories. Each category contains specific semantic and conceptual processing methods.

2.1 Definition and significance of data

In the DIKWP model, data is defined as the specific manifestation of expressing the same meaning in cognitive processes. This definition emphasizes that data is not just a simple record of facts or observations, but the result of semantic matching and probabilistic confirmation of these records through cognitive agents (such as humans or artificial intelligence systems).

2.2 The cognitive process of data

Data is not an independent factual record in cognitive space, but is given meaning through the cognitive process of the cognitive subject. The cognitive process includes the following steps:

Data recording and observation: Firstly, data is collected as observations and records of the real world. This is the original state of the data.

Semantic classification and matching: Cognitive subjects compare and match these data with their existing concepts and semantics. This process involves identifying the feature semantics of the data and categorizing it into a specific concept.

Concept confirmation: Through semantic confirmation of data features, the cognitive subject categorizes them into a specific conceptual space. This confirmation process relies on the cognitive subject's background knowledge and semantic understanding.

2.3 Specific examples

Taking the concept of "sheep" as an example, when a cognitive subject sees a group of sheep, they will classify these individuals as "sheep" based on their existing semantic understanding (such as body shape, color, behavior, etc.). Even if each sheep has different specific features, the cognitive subject can still classify them into the same category through the same semantic features. This process emphasizes the subjectivity and semantic dependency of data in the conceptualization process.

3 The distinction between conceptual space and semantic space

In the DIKWP model, concept space and semantic space are two different but related levels:

Conceptual space: Conceptual space refers to the place where cognitive subjects use natural language and other forms of communication. The data concept here represents the semantic confirmation of specific facts or observations in the cognitive subject's conceptual space.

Semantic space: Semantic space is the place within the cognitive subject for understanding concepts and corresponding semantics. The semantics of data are considered as specific manifestations of the same semantic set in cognitive processes in the semantic space.

Mathematical representation of concepts and semantics

The semantics of data can be represented by a feature semantic set S:

S={f₁,f₂,…,f_n}

Each f_i represents a feature semantic of the data. The dataset D is composed of specific instances that share the same semantic attribute set S:

D={d∣d share S}

This mathematical representation helps us understand how data is induced and processed based on shared semantic features in the cognitive and processing processes.

4 The distinction between data concepts and data semantics

In the DIKWP model, the distinction between data concepts and data semantics is the basis for the transformation of cognitive processes towards conceptual and semantic space processing:

Data concept: Data concept is regarded as a concrete conceptual mapping of the same semantics in cognitive processes in the conceptual space. They represent the cognitive processing results of the cognitive subject in observing and recording the real world.

Data semantics: Data semantics are considered as specific manifestations of the same set of semantics in cognitive processes in the semantic space. They are the meanings obtained through semantic matching and confirmation in the cognitive process of data.

5 Cognitive attributes of data

The cognitive properties of data emphasize the importance of data in the interaction between cognitive subjects and conceptual and semantic spaces. This attribute includes:

Subjectivity: The cognition of data is not only a passive recording, but also a process in which the cognitive subject actively seeks semantic features that match known cognitive objects. The background knowledge and experience of cognitive subjects play a crucial role in the process of data cognition.

Context dependency: The cognitive meaning of data depends on specific context and context. In different cognitive subjects or backgrounds, the same data concept may be associated with different semantics.

6 The cognitive value of data concepts

In the DIKWP model, the cognitive value of data concepts lies not only in their physical form or function, but also in how they connect with the existing knowledge system of the cognitive subject, and are then recognized and confirmed as objects or concepts with specific semantics. This perspective breaks through the traditional concept of data and emphasizes the semantic interaction between data and cognitive subjects.

Platonic ideas

This understanding highlights the Platonic idea that things in the real world are only shadows of their ideas (i.e., "the same semantics"). The cognitive value of data concepts lies not only in their objective existence, but also in how cognitive subjects seek and confirm the common semantics of cognitive objects and phenomena through data concepts, triggering semantic resonance and cognitive confirmation.

7 Mathematical representation of data

The mathematical representation of data concepts emphasizes the semantic multidimensional and structural nature of data in the cognitive process. By treating data concepts as a collection of objects sharing the same semantic features, we can better understand the induction and classification process of data in cognitive processing.

Semantic Vector Space: Data concepts can be represented as a semantic vector space, where each element is a semantic instance. These instances are classified under the same semantic attribute by sharing one or more semantic features.

Feature semantic set: The feature semantic set S defines the semantic features of data concepts. These features help cognitive subjects recognize and classify data concepts in the semantic space.

8 Data concepts and semantic recognition

The formation and understanding of data concepts is not only about recording objective facts, but also involves how cognitive subjects match the semantics of these facts with existing semantic cognitive structures. This process emphasizes the importance of semantic recognition, where the cognitive subject identifies and classifies objects through semantic features in data concepts.

Semantic matching and probability confirmation

In the DIKWP model, the recognition and processing of data concepts rely on the cognitive subject's matching and probability confirmation of the same semantics. The cognitive subject identifies specific concepts through semantic confirmation of data features, and classifies and processes them. This semantic matching process is not only an accurate semantic correspondence, but can also be an approximate matching based on probability.

The DIKWP model proposed by Professor Yucong Duan emphasizes the subjectivity and semantic dependence of data by defining data concepts and semantics in detail, highlighting the importance of data in the interaction between cognitive subjects and conceptual and semantic spaces. Through this definition and model, we can gain a deeper understanding of the role of data in cognitive processes and its relationship with semantics and concepts.

This perspective not only provides a new theoretical framework for cognitive science and philosophy, but also provides a theoretical foundation for data processing in the fields of natural language processing and artificial intelligence. If you have more specific questions or need to further explore a certain aspect, please feel free to raise them.

9 Case: pedestrian recognition in autonomous vehicle

9.1 Scene description

In autonomous vehicle, pedestrian recognition is a key task. Sensors in cars (such as cameras, Laser Radar, etc.) collect a large amount of environmental data, including pedestrian images, distance information, etc. In order to ensure safety, the auto drive system needs to accurately identify and classify these data, judge which objects are pedestrians, and take corresponding actions.

9.2 Data collection and initial recording

First, the sensors of autonomous vehicle collect a large amount of raw data. These data include:

Camera image: Multiple environmental images captured per second.

Laser Radar data: reflects the distance and shape of objects in the environment.

At this stage, these data are only objective records of the environment and have not been processed or classified in any way.

9.3 Semantic classification and matching of data

In the DIKWP model, these raw data need to be given meaning through semantic matching and conceptual confirmation.

Feature extraction of data: The auto drive system will extract features from camera images and Laser Radar data. For example, a certain area in an image contains certain features (such as shape, color, motion patterns, etc.) that may be associated with the concept of "pedestrians".

Semantic feature set: The extracted features can be represented as a semantic feature set S, for example:

S={Height, width, direction of motion, color mode}

Each feature f_i represents a specific semantic attribute.

Semantic matching: The system will semantically match these features with existing pedestrian models. The pedestrian model is trained based on a large amount of known pedestrian data and includes various semantic features of pedestrians. For example, a height between 1.5 meters and 2 meters, a width between 0.5 meters and 1 meter, and a certain color pattern.

Concept confirmation

After semantic matching, the system confirms that objects in a certain area are "pedestrians". This confirmation is based on the same semantics of features, such as:

The height of the object is between 1.5 meters and 2 meters.

The width of the object is between 0.5 meters and 1 meter.

The motion pattern of objects is consistent with the motion characteristics of pedestrians.

This semantic matching and validation process involves the background knowledge and pre training model of the cognitive subject (i.e. auto drive system).

9.4 The subjectivity and contextual dependence of data

It is worth noting that this process is not entirely objective. The recognition and confirmation of data depend on the pre trained model and context of the system. For example:

In certain scenarios, such as crowded streets, the system may require higher accuracy to distinguish pedestrians from other objects.

Different auto drive system may use different training data and models, resulting in different pedestrian recognition results in the same environment.

9.5 Mathematical representation of data

In mathematics, we can represent the semantics of data as a vector space. Assuming the system extracts the following semantic features:

Height: 1.7 meters

Width: 0.6 meters

Color mode: Most are light colors

Movement direction: moving forward

These features can be represented as vectors d:

D=(1.7,0.6, light color, forward)

By matching with the semantic feature set S in the pre trained model:

S={(height, 1.5 ≤ height ≤ 2.0), (width, 0.5 ≤ width ≤ 1.0), color mode, motion mode}

If the features in d match those in S, the system confirms that the object is a pedestrian.

9.6 The interaction between data concepts and semantic recognition

This process demonstrates the interaction between data concepts and semantic recognition in the DIKWP model:

Data concept: The process by which a system identifies objects as pedestrians represents the cognitive processing results of observing and recording the real world.

Data semantics: These semantic features (height, width, color pattern, motion direction) represent the specific manifestations of the same semantic set in the cognitive process.

Through the case of pedestrian recognition in autonomous vehicle, we elaborated the definition of "data" in DIKWP model and its role in cognition, concept and semantic space. This process is not only the recording of objective data, but also involves semantic matching and concept confirmation of cognitive subjects (auto drive system), emphasizing the subjectivity and context dependence of data. This perspective not only provides a new theoretical framework for cognitive science and philosophy, but also provides a theoretical foundation for data processing in autonomous driving technology.

10 Related work

Regarding the relevant work on the DIKWP model and its processing of data, semantics, and concepts, the following key areas of research can be referred to, which provide important background for understanding the data processing in the DIKWP model.

10.1 Data-Information-Knowledge-Wisdom (DIKW) model

The DIKW model is a classic model in information science and knowledge management, describing the transformation process from data to Wisdom. The basic idea of this model is:

Data: raw, unprocessed facts and observations.

Information: Making data meaningful by processing and organizing it.

Knowledge: By interpreting and analyzing information, one can gain a higher level of understanding.

Wisdom: Applying knowledge to make wise decisions.

Related research

Ackoff, R. L. (1989). "From Data to Wisdom". Journal of Applied Systems Analysis. Ackoff proposed the basic concept of the DIKW model, emphasizing the transformation process at each level.

Rowley, J. (2007). "The wisdom hierarchy: representations of the DIKW hierarchy". Journal of Information Science. Rowley provided a detailed overview of the DIKW model, discussing the characteristics and interrelationships of each level.

10.2 Semantic web and ontology

Semantic web technology focuses on how to achieve interoperability and understanding between data by defining and linking the semantics of data. This is closely related to the emphasis on data semantics in the DIKWP model.

Related research

Berners-Lee, T., Hendler, J., & Lassila, O. (2001). "The Semantic Web". Scientific American. This article lays the foundation for the Semantic Web and discusses methods for linking data through ontology and standardized semantics.

Gruber, T. R. (1993). "A translation approach to portable ontology specifications". Knowledge Acquisition. Gruber proposed the concept of ontology and defined a framework for shared semantics for data exchange and understanding between different systems.

10.3 Semantic memory in cognitive science

Cognitive science studies how to store and process semantic information in the brain, which is related to the semantic processing of data by cognitive subjects in the DIKWP model.

Related research

Tulving, E. (1972). "Episodic and semantic memory". Organization of Memory. Tulving distinguished situational memory from semantic memory and discussed the processing of semantic information in the brain.

Collins, A. M., & Quillian, M. R. (1969). "Retrieval time from semantic memory". Journal of Verbal Learning and Verbal Behavior. They proposed a semantic network model that explains the organization and retrieval process of semantic information in the brain.

10.4 Knowledge representation and inference in artificial intelligence

The research on knowledge representation and inference in the field of AI is closely related to the data concept processing in the DIKWP model, as it involves formalizing symbol systems to represent and manipulate knowledge.

Related research

Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning. This book comprehensively introduces the basic concepts and methods of knowledge representation and inference, and discusses how to represent and manipulate semantic information in AI systems.

Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. This book is a classic textbook in the field of AI, covering various aspects from data representation to advanced reasoning.

10.5 Semantic Analysis in Natural Language Processing

NLP studies how to understand and generate semantics in natural language texts, which is related to the recognition and matching of data semantics in the DIKWP model.

Related research

Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. This book introduces statistical methods in NLP and discusses how to understand text data through semantic analysis.

Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing. This book is an important textbook in the field of NLP, covering various aspects from lexical semantics to text comprehension.

By reviewing these important related works, we can gain a more comprehensive understanding of the theoretical background of data processing in the DIKWP model. These studies provide important theoretical support for semantic classification, conceptual confirmation, and cognitive processing of data, helping us understand the subjectivity and contextual dependencies of data in the DIKWP model. If further exploration of a specific field of research is needed, please feel free to ask more questions.

11 Comparative analysis

The definition of data in Professor Yucong Duan's DIKWP model has many unique features compared to traditional data definitions. In order to better understand and compare, we will conduct an in-depth analysis of the data definitions in the DIKWP model and other related works, especially in the fields of DIKWP models, semantic web, ontology, cognitive science, and knowledge representation in artificial intelligence.

11.1 Data definition in the DIKWP model

In the DIKWP model, DIKWP-Data is defined as the specific manifestation of expressing the same meaning in the cognitive process. This definition emphasizes that data is not only a simple factual record, but also the result of semantic matching and probability confirmation of these records through cognitive subjects. The following are the key points of this definition:

Data in the cognitive process: Data is regarded as the observation and recording of the external world by cognitive entities (such as humans or AI systems) during the cognitive process.

Semantic matching and concept confirmation: The meaning of data is confirmed through the classification and matching of its semantic features by cognitive subjects. This process involves semantic recognition and conceptualization of features.

Subjectivity and contextual dependence: The cognition and processing of data depend on the background knowledge and semantic space of the cognitive subject, and have subjectivity and contextual dependence.

11.2 Data definition in the DIKW model

The DIKW model is a classic model consisting of four levels: data, information, knowledge, and wisdom. Its definition of data is as follows:

Data: Data is raw, unprocessed facts and observations. They exist objectively and have not been processed or explained.

Information: When data is processed and given meaning, it becomes information. Information is data with specific context and purpose.

Knowledge and Wisdom: Further explanation and analysis of information, forming knowledge, and finally obtaining Wisdom through the application of knowledge.

Comparative analysis

Objectivity vs. Subjectivity: In the DIKW model, data is an objective record, while in the DIKWP model, the meaning of data depends on the semantic matching of cognitive subjects and has subjectivity.

Processing: The DIKW model regards data as the foundation of information, and the processing goes from data to information, while the DIKWP model emphasizes that the data itself already includes semantic processing in the cognitive process.

11.3 Data definition in semantic web and ontology

Semantic web technology and ontology focus on the semantic linking and interoperability of data. Ontology defines the semantics of data, allowing different systems to understand and process this data.

Data: Data is considered as entities with semantic labels that define the meaning and purpose of the data.

Semantic linking: Data is associated with other data through ontology and semantic linking, achieving semantic interoperability.

Comparative analysis

Semantic Clarity: In the semantic web and ontology, the semantics of data are clear and standardized, defined through ontology. In the DIKWP model, the semantics of the data are obtained through semantic matching and probability confirmation by cognitive agents.

Interoperability: The Semantic Web emphasizes data interoperability and standardized semantic linking, while the DIKWP model focuses on the semantic processing of data within cognitive entities.

11.4 Data definition in cognitive science

Cognitive science studies the processing and storage of data in the brain, particularly how semantic memory organizes and retrieves data.

Data: In cognitive science, data is viewed as semantic and situational memory stored in the brain.

Semantic memory: Semantic memory involves the organization and retrieval of data, understanding the storage and processing of data through semantic network models.

Comparative analysis

Data storage and processing: Cognitive science focuses on the storage and processing of data in the brain, emphasizing semantic memory. The DIKWP model emphasizes the processing of data through semantic matching and conceptual confirmation in the cognitive process.

Semantic Network Model: The semantic network model in cognitive science shares similarities with the semantic matching process in the DIKWP model, but the DIKWP model focuses more on the subjectivity and contextual dependencies of data.

11.5 Data definition in artificial intelligence

The study of knowledge representation and inference in artificial intelligence (AI) explores how to represent and manipulate data through formal symbol systems.

Data: In AI, data is the fundamental entity used for training models and reasoning, typically represented through feature vectors.

Knowledge representation and inference: Data is formalized into logic and rules through knowledge representation for inference and decision-making.

Comparative analysis

Formal representation: Data in AI is usually represented formally through feature vectors and logical rules, while the DIKWP model emphasizes semantic matching and subjective processing of data.

Training and inference: AI focuses on the training and inference process of data, while the DIKWP model focuses on the semantic confirmation and processing of data within the cognitive subject.

12 Overview Summary

Professor Yucong Duan's definition of data in the DIKWP model has unique characteristics, emphasizing the subjectivity, semantic matching, and contextual dependence of data. This is significantly different from the data definitions in traditional DIKW models, semantic web, ontology, cognitive science, and artificial intelligence. Specifically, as follows:

Subjectivity: The DIKWP model emphasizes the subjectivity of data, and the meaning of data is determined through semantic matching and confirmation by cognitive subjects, while traditional models often emphasize the objectivity of data.

Semantic processing: The DIKWP model embeds semantic processing of data into cognitive processes, while other models often assign and process semantics after the data.

Context dependency: The DIKWP model emphasizes context dependency in data processing, as data from different backgrounds may have different semantics, which traditional models often overlook.

By comparison, we can see that the DIKWP model places more emphasis on the interaction between semantics and cognition in the process of processing data, providing a more dynamic and flexible perspective to understand the role and significance of data. If you need more detailed cases or further exploration in specific fields, please feel free to continue asking questions.

The following is a table for a detailed comparative analysis of the data definitions in Professor Yucong Duan's DIKWP model with those in traditional DIKWP models, semantic web and ontology, cognitive science, and artificial intelligence:

Feature/Model	DIKWP Model	DIKW Model	Semantic Web and Ontologies	Cognitive Science	Artificial Intelligence
Data Definition	Concrete manifestations of "same" meaning in cognition, determined by semantic matching and probability confirmation by the cognizer	Raw, unprocessed facts and observations	Entities with semantic tags, defined semantically and by purpose via ontologies	Semantic and situational memories stored in the brain	Basic entities used for training models and inference, represented by feature vectors
Subjectivity	High, meaning of data depends on cognizer's semantic matching and background knowledge	Low, data is considered objectively existing	Medium, semantics of data defined by standardized ontologies, but interpretation may vary	Medium, semantics of memories depend on individual cognitive structures	Low, representation of data vectors is usually objective, though training data may be biased
Context Dependency	High, semantics of data may differ in different backgrounds	Low, data lacks context before processing	High, data's semantics depend on its position and relations within the ontology	High, semantics of data depend on the cognizer's background and experiences	Medium, feature vectors of data may vary across different application scenarios
Processing	Data is given meaning through semantic matching and concept confirmation after collection	Data becomes information through processing and organization	Data achieves interoperability and understanding through ontological definitions and semantic linking	Data stored and retrieved through brain's semantic network models	Data processed through feature extraction and logical rules for training and inference
Semantic Matching and Confirmation	Semantics of data determined through cognizer's matching and confirmation	Data becomes information after processing and interpretation	Semantics of data explicitly defined by ontology and standardized tags	Semantics of data matched and retrieved through semantic memory and network models	Data classified and matched through feature vectors and training models
Interoperability	Low, mainly focuses on semantic processing within the cognizer	Not involved in interoperability	High, data achieves interoperability through standardized ontologies and semantic linking	Not involved in interoperability	Medium, interoperability depends on training models and feature representations
Mathematical Representation of Data	Represented as a set of semantic features (e.g., S={f₁, f₂, …, f_n})	No explicit mathematical representation, data considered as raw material	Semantic entity sets defined by ontologies	Represented by semantic network models showing relationships between data	Data represented by feature vectors, trained and inferred through vector space models
Complexity of Processing and Analysis	High, involves comprehensive processing including semantic matching, concept confirmation, and background knowledge	Medium, direct processing from data to information	High, involves ontological definitions, semantic linking, and standardized processing	High, involves organization and retrieval of semantic memories	High, involves feature extraction, model training, and logical reasoning
Cognitive Value of Data Concept	Emphasizes the internal semantic interactions and contextual associations within the cognizer	Data is the basis of information, but is not inherently meaningful	Emphasizes understanding and interoperability of data across different systems through standardized semantic links	Emphasizes the storage and semantic relationships of data in the brain	Emphasizes the feature representation and application of data in model training and inference

13 Detailed comparative analysis

13.1 Data definition

The DIKWP model: Data is defined as the specific manifestation of expressing the same meaning in cognitive processes, emphasizing semantic matching and conceptual confirmation.

DIKW model: Data is raw, unprocessed facts and observations, emphasizing objective recording.

Semantic Web and Ontology: Data defines semantics and purposes through ontology, with clear semantic labels.

Cognitive science: Data is the semantic and situational memory stored in the brain.

Artificial intelligence: Data is represented as feature vectors for model training and inference.

13.2 Subjectivity

The DIKWP model: The meaning of data highly depends on the semantic matching and background knowledge of cognitive subjects, and has high subjectivity.

DIKW model: Data is considered to exist objectively and has low subjectivity.

Semantic Web and Ontology: The semantics of data are defined through standardized ontology, with moderate subjectivity.

Cognitive science: The semantic memory of data depends on an individual's cognitive structure, with moderate subjectivity.

Artificial intelligence: The representation of data feature vectors is usually objective, but training data may have biases and low subjectivity.

13.3Context dependency

The DIKWP model: The semantics of data are highly dependent on context, and the semantics may vary under different backgrounds.

DIKW model: Data has no context dependency before processing.

Semantic Web and Ontology: The semantics of data depend on its position and association in the ontology, with high context dependency.

Cognitive science: The semantics of data depend on the background and experience of the cognitives, with high contextual dependence.

Artificial intelligence: The feature vectors of data may vary in different application scenarios, with moderate context dependency.

13.4 Processing process

DIKWP model: Data is assigned meaning through semantic matching and conceptual confirmation after collection.

The DIKW model: Data is processed and organized into information.

Semantic Web and Ontology: Data achieves interoperability and understanding through ontology definitions and semantic links.

Cognitive science: Data is stored and retrieved in the brain through semantic network models.

Artificial intelligence: Data is trained and inferred through feature extraction and logical rules.

13.5 Semantic matching and confirmation

The DIKWP model: The semantics of data are obtained through the matching and confirmation of cognitive subjects.

DIKW model: Data is processed and interpreted to become information.

Semantic Web and Ontology: The semantics of data are defined by ontology and standardized labels.

Cognitive science: The semantics of data are matched and retrieved through semantic memory and network models.

Artificial intelligence: Data is matched and classified through feature vectors and training models.

13.6 Interoperability

The DIKWP model mainly focuses on semantic processing within cognitive subjects, with low interoperability.

DIKW model: does not involve interoperability.

Semantic Web and Ontology: Achieving high interoperability through standardized ontology and semantic linking.

Cognitive science: does not involve interoperability.

Artificial intelligence: Data interoperability relies on training models and feature representations, with moderate interoperability.

13.7 Mathematical representation of data

DIKWP model: represented by a semantic feature set, such as S={f₁, f₂,..., f_n}.

DIKW model: without clear mathematical representation, data serves as the basic raw material.

Semantic Web and Ontology: A collection of semantic entities defined by ontology.

Cognitive science: Semantic network models represent the relationships between data.

Artificial intelligence: Representing data through feature vectors for training and inference.

13.8 The complexity of processing and analysis

The DIKWP model involves comprehensive processing of semantic matching, concept confirmation, and background knowledge, with high complexity.

The DIKW model: The processing of data to information is relatively direct and has moderate complexity.

Semantic web and ontology: involving ontology definition, semantic linking, and standardization processing, with high complexity.

Cognitive science: involves the organization and retrieval of semantic memory, with high complexity.

Artificial intelligence: involves feature extraction, model training, and logical reasoning, with high complexity.

By comparing and analyzing Professor Yucong Duan's DIKWP model with traditional data models, we found that the DIKWP model has unique characteristics in data definition, emphasizing the subjectivity, semantic matching, and contextual dependence of data. This model breaks through the objective definition of data in the traditional DIKW model, and emphasizes the cognitive value of data through semantic confirmation and concept processing in the cognitive process. The semantic web and ontology provide standardized semantic links, but the DIKWP model focuses more on the semantic interaction of data within cognitive subjects. Although the data definitions in cognitive science and artificial intelligence overlap with the DIKWP model in some aspects, the DIKWP model places more emphasis on the subjectivity and contextual relevance of the data.

Conclusion

In summary, the DIKWP model provides a new perspective and theoretical framework for data processing and cognitive science, helps deepen understanding of the role and significance of data in cognitive processes, and provides new research directions for fields such as natural language processing and artificial intelligence. Future research can further explore the practical application and optimization of the DIKWP model in specific applications, promoting the development and innovation of data processing technology.

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