1. Introduction
“Knowledge is Power” - Francis Bacon.
Engineering problems cannot be solved without applying knowledge. Consequently, knowledge-intensive activities, such as knowledge acquisition, representation, dissemination, utilization, and management, play a vital role in engineering problem solving. The advent of systems engineering (de Weck, 2018), engineering informatics (Tomiyama et al., 2012), as well as the fourth industrial revolution (smart manufacturing, Industry 4.0) (Zhou et al., 2018; Sinclair et al., 2019; Ullah, 2019) has added a new dimension—digitization of knowledge-intensive activities by applying advanced computing as well as information and communication technologies. As far as Industry 4.0 is concerned, it (Industry 4.0) employs some embedded systems (e.g., cyber-physical systems) to perform such cognitive tasks as monitoring, understanding, predicting, deciding, acting, and adapting (Zhou et al., 2018; Sinclair et al., 2019; Ullah, 2019). Without applying digitized knowledge, these systems cannot work. As a result, the digitization of knowledge-intensive activities (knowledge acquisition, representation, dissemination, utilization, and management) is critical for Industry 4.0. Before developing methods and tools, which are needed for achieving the desired level of digitization of knowledge-intensive activities in Industry 4.0, the following questions must be addressed. What is knowledge? What are the types of knowledge? How to create knowledge? How to represent knowledge? What is the difference between data/information and knowledge? What is the role of human cognition in knowledge formation? What is the role of experience in knowledge formation? Is the attainment of “true” knowledge possible? Is analytical knowledge better than experiential knowledge?
The abovementioned questions are difficult to answer since a relatively unambiguous and circularity-free definition of knowledge is not yet available. This can be understood from the commonly used definitions of knowledge, as described below. A compressive account on the definition of knowledge is presented in Section 2.
Consider the following three general views regarding knowledge. 1) First, consider the most general view regarding knowledge, that is, a piece of knowledge is a proposition that corresponds to justified true belief (Gettier, 1963). The process of justifying the truthfulness of a belief involves several intellectual resources, and the process capable of making justification of a belief possible many not be known beforehand. Thus, “true knowledge” may not exist. 2) Secondly, consider the dictionary meaning of knowledge. For example, a dictionary-based definition describes knowledge as an awareness, understanding, or information, which either resides within a person’s mind or is possessed by people and can only obtained via experience or investigation (Knowledge definition and meaning , 2019). This is a rather broad definition of knowledge involving other concepts requiring prior definition. 3) Lastly, consider the definitions of knowledge given by legislative bodies. For example, the European Union defines knowledge as facts, principles, theories, and practices accumulated by learning; both cognitive reflections and direct experiences of individuals or groups contribute to the body of knowledge (Abele et al., 2017).
The remarkable thing is that all definitions of knowledge (including those presented in Section 2) are based on several concepts. For example, the last definition mentioned above associates concepts, such as learning, cognitive reflection, direct experience, fact, principle, theory, and practice, to define the knowledge. Such concepts must be defined before defining knowledge. This results in a phenomenon called circularity that must be avoided while defining knowledge (Zagzebski, 1999). Therefore, defining knowledge in clear terms, at the same time avoiding circularly, is a challenging task. This paper aims to present a circularity-free and unambiguous definition of knowledge that can help build knowledge-based systems from the context of Industry 4.0.
The remainder of this paper is organized as follows. Section 2 presents a comprehensive review of definitions of knowledge reported in extant literature concerning epistemology, engineering design, manufacturing, as well as organization science, education science, and information science. Section 3 presents a revised definition of knowledge along with its different types and categories. Section 4 describes the types and categories of knowledge presented in Section 3 using some real-life examples. The representation of knowledge using knowledge graphs (concept maps) is also presented in Section 4. Section 5 discusses the implications of this study by demonstrating the existence of different types and categories of knowledge in a creative design process. This section also suggests a framework for developing knowledge-based systems for the advancement of Industry 4.0. Section 6 presents the concluding remarks drawn from this study.
2. Literature Review
Three commonly known definitions of knowledge are very briefly presented in the previous section. However, the concept of knowledge and its definitions have been studied by many stakeholders at great depth. This section thus, first, reviews the definitions of knowledge found in epistemology. Subsequently, it reviews the definitions of knowledge found in the literature of engineering design, manufacturing, and other relevant fields such as organization, information, and education sciences.
2.1. Epistemology
Epistemology is the philosophical study that deals with the nature, origin, and formulation of knowledge irrespective of the academic discipline (Sosa, 2017; Steup, 2018). The definition of knowledge in epistemology exhibits multiplicity, which has lasted since the period of Aristotle. Multiplicity originates from such metaphysical concepts as idealism, rationalism, empiricism, neutralism, pragmatism or evolutionism, and explanationism. Each metaphysical concept corresponds to certain truths that manifest knowledge. In particular, idealism considers there exist unquestionable and transcendental truths that are entirely independent of experiences. Rationalism considers there exist rational processes that are somewhat independent of experiences, thereby leading to some truths. Empiricism considers all truths to be dependent on experiences; that is, the experience is the sole driver that contributes to knowledge formation. Pragmatism adopts a skeptic or evolutionary view toward truth; that is, usefulness of the perceived truth determines its fate—whether or not it will be considered a piece of knowledge. Consequently, truthfulness may vary with time. Neutralism is similar to pragmatism, and considers that while finding truth, any metaphysical concept from amongst idealism, rationalism, and empiricism can be used. This implies that truth is not biased to a specific metaphysical concept, and that any combination of metaphysics can be used to formulate knowledge. Explanationism considers that a so-called scientific truth evolves in accordance with the deductive–nomological (D–N) explanation, inductive­–statistical (I–S) explanation, or statistical–relevance (S–R) explanation (Hempel, 1968; Salmon et al., 1971; Shrader, 1977; Salmon, 2006; Woodward 2017).
In classical epistemology, definitions of knowledge proposed by Hume and Kant have attracted significant attention. According to Hume, knowledge corresponds to two propositions—relations of ideas and matters of fact (Locke et al., 1960). Relations of ideas are a priori non-falsifiable propositions (e.g., a triangle has three sides, summation of all included angles of a triangle equals 180°, etc.). Matters of fact are experience-dependent propositions that can be falsified if a counterexample is available (e.g., apples are good for health, bachelors are messy, etc.). Kant, on the other hand, analyzed the work of Hume and proposed there exist three types of knowledge—analytic a priori, synthetic a priori, and synthetic a posteriori (Kant, 2000). Analytic a priori knowledge is always true, because these exist mere definitions of ideas (e.g., a triangle has three sides, all unmarried males are bachelors, etc.). Synthetic a priori knowledge is deduced from a set of analytic a priori knowledge (e.g., 4 + 7 = 11, summation of all included angles of a triangle equals 180°, etc.). Thus, knowledge gained from mathematical and geometric derivations falls under the category of synthetic a priori knowledge. Synthetic a posteriori knowledge corresponds to knowledge gained through experience (e.g., apple is good for health, bachelors are rich, etc.). Besides, Kant considered existence of four concepts or categories of pure understanding—quantity, quality, relation, and modality—which correspond to the inherent ability of humans to organize their experiences and formulate synthetic a posteriori knowledge. These four categories, in turn, entail twelve concepts of judgment. Specifically, quantity entails the concepts of unity, plurality, and totality; quality entails reality, negation, and limitation; relation entails inherence and subsistence, cause and effect, and community; and lastly, modality entails possibility–impossibility, existence–nonexistence, and necessity and contingency. Although Hume and Kant are considered an empiricist and rationalist, respectively, their definitions of knowledge possess certain similarities. For example, Hume’s relations of ideas correspond to experience-independent knowledge, which Kant classified into two categories—analytic a priori and synthetic a priori. Both Hume and Kant categorized experience-dependent knowledge into a separate category. Hume classified it as matters of fact, whereas Kant considered it as synthetic a posteriori.
Apart from the Hume- and Kant-based definitions of knowledge, there exist other definitions of knowledge in epistemology. According to Russell (1911 (reprinted 2015) and 1914 (reprinted 2005)), there exist two types of knowledge—by acquaintance and by description. Knowledge by acquaintance implies knowledge gained by direct awareness or experience of a knower, and is free from any intermediary inference processes. Knowledge by description, in contrast, is a propositional truth acquired via inferential, mediated, or indirect processes. Such definitions of knowledge explicitly specify the role of the knower in knowledge formulation. Many authors have investigated knowledge from perspectives of acquaintance and description, and provided epistemological descriptions of acquaintance and descriptive knowledge (Gertler, 1999; BonJour, 2003; Fumerton, 2005). Meanwhile, new metaphysics have also been added whilst formulating knowledge by prioritizing the knower. For example, Zagzebski (1999) considered that knowledge formulates when knowers try to build a relationship with a portion of reality through their consciousness. Knowers might directly or indirectly be related to a portion of reality. Therefore, knowledge depends on knowers’ cognitive abilities and their emotional attachment with a portion of reality; that is, the role of the knower must be quantified while defining knowledge. Accordingly, Zagzebski (1999) defined knowledge as a cognitive contact with reality arising out of acts of intellectual virtue. This implies that “intellectual virtue” is a metaphysical quantifier of the knower with regard to knowledge formulation. However, intellectual virtue can be defined in two different ways (Battaly, 2018) that depend on the concept of reliability (Sosa, 1991; Greco, 1993) and responsibility (Zagzebski, 1999; Battaly, 2018). The above definition of knowledge is based on responsibility (open-mindedness, courage, critical thinking, moral obligation, etc.).
2.2. Engineering Design and Manufacturing
Like its predecessors, in Industry 4.0, it is highly likely that seamless execution of engineering and manufacturing (i.e., product, system, and service conceptualization and realization) gets the highest priority. Thus, how the concept of knowledge has been treated in engineering design and manufacturing must be elucidated before proposition a clear and circularity-free definition of knowledge.
First, consider engineering design (product, system, and service conceptualization). Engineering design is a purely knowledge-intensive activity (Tomiyama et al., 2013). Therefore, certain design theories explicitly highlight the contribution of knowledge in the execution of a design process. For example, let us consider the general design theory (Yoshikawa, 1989; Takeda et al., 1990; Reich, 1995) and C–K theory of design (Hatchuel and Weil, 2008; Ullah et al., 2012; Agogue and Kazakci, 2014; Le Masson et al., 2017). In accordance with the general design theory (Yoshikawa, 1989), execution of a design process requires knowledge manipulation, wherein “knowledge” may either to be of the ideal or real types (Takeda et al., 1990; Reich, 1995). This ideal/real knowledge plays its role through logical processes of deduction and abduction (Takeda et al., 1990), as described in Section 3. Both knowledge types assist in making necessary decisions concerning the continuation of a design process under given circumstances (Takeda et al., 1990). Nevertheless, ideal or real knowledge types can be defined with respect to other concepts, such as the “entity” and “topology” of the design space (Reich, 1995), which must be defined prior to defining the knowledge types. This injects circularity in the definition of ideal or real knowledge, in addition to certain logical ambiguities caused by induction (Section 3). In addition to deduction and abduction (refer to Section 3 for definitions), another logical process called induction (Ullah, 2007) must be considered when processing real knowledge. This is because induction extracts knowledge from experiences and experimental data. Additionally, the role of induction is not explicitly highlighted when processing real knowledge within the framework of the general design theory, thereby imparting ambiguity in the general-design-theory-based definition of knowledge. In contrast, the C–K theory of design considers the simultaneous evolution of two domains—concept and knowledge—when a design process continues (Hatchuel and Weil, 2008; Ullah et al., 2012). Application of this theory requires two knowledge types—existing and new—for continuing a design process (Hatchuel and Weil, 2018; Ullah et al., 2012; Agogue and Kazakci, 2014; Le Masson et al., 2017). New knowledge is necessary to resolve epistemic uncertainties underlying creative concepts (Ullah et al., 2012). Unlike the general design theory, the C–K theory does not define new-knowledge creation or existing-knowledge utilization processes in terms of deduction, abduction, induction, and so on. Consequently, C–K-theory-based definitions of knowledge are somewhat informal.
Similar to engineering design, in manufacturing (product, system, service realization), the concept of knowledge has always existed. It appears more explicitly owing to the advent of Industry 4.0. As mentioned before, Industry 4.0 employs embedded systems (e.g., cyber-physical systems) to execute such cognitive tasks as monitoring, understanding, predicting, deciding, acting, and adapting. Some authors reckon that the cyber-physical systems are nothing but an extensive and self-growing knowledge base (Zhou et al., 2018), but knowledge is not defined in clear terms. On the other hand, some authors consider that the contents by which the embedded systems perform, take the form of digital twins—exact mirror images of real-world objects, processes, and phenomena—in cyberspace (Ghosh et al., 2019; Ullah, 2019). Some of these twins consists of different types of knowledge (Ullah, 2019), but these types are not clearly defined. Some other authors (e.g., consider the work in Zheng et al., 2018) reckon that both data-bases and knowledge-bases must populate the embedded systems where the demarcation lines between “data” and “knowledge” are not clearly drawn. As a result, in the literature of Industry 4.0, the concept of knowledge remains ambiguous.
2.3. Other Relevant Fields
In addition to epistemology, engineering design, and manufacturing, definitions of knowledge have also been reported in the literature of other fields, such as organization, education, and information sciences.
A well-known definition of knowledge in organization science states that knowledge can either be of the tacit or explicit types (Polanyi, 1958; Nonaka, 1994; Nonaka and Takeuchi, 1995; Randles et al., 2012; Nonaka et al., 2014). Tacit knowledge pertains to intuitions, experiences, and know-how possessed by active individuals in their respective organizations. Consequently, it is challenging to identify or even codify such knowledge (Polanyi, 1958; Nonaka, 1994; Randles et al., 2012). Explicit knowledge includes documented instructions for facilitating organizational activities. It is, therefore, easy to identify and share. Tacit knowledge dynamically transforms into explicit knowledge and vice versa through social or teamwork-based interactions (dialogue) among employees (Nonaka et al., 2014). It is remarkable that such transformations do not require formal logical processes to be performed (Nonaka, 1994; Nonaka and Takeuchi, 1995). This contradicts the definitions of knowledge reported in other disciplines, such as information science. However, there exists other schools of thought in organization science related to knowledge (Albino et al., 2001) and its formation (Bohn, 1994). For example, Albino et al. (2001) considered that there exist five types of knowledge—scientific, quantitative, qualitative, tacit, and intuitive. As reported by Bohn (1994), the knowledge formation and validation processes follow a hierarchy, which is not clearly defined.
In education science, the concept of knowledge has always existed along with human-learning. For example, consider the definitions of knowledge presented in Carson (2004), Kinchin et al. (2019), and Ullah (2019). Carson (2004) has proposed nine categories of knowledge—empirical, rational, conventional, conceptual, cognitive-process skills, psychomotor, affective, narrative, and received. All these categories of knowledge form in the intertwined domains, and ultimately transform to conventional knowledge. Kinchin et al. (2019) have proposed four types of knowledge, namely, novice knowledge, theoretical knowledge, practical knowledge, and professional knowledge. All these types of knowledge possess different degrees of “semantic gravity.” Ullah (2019) has proposed five types of knowledge—analytic a priori, synthetic a priori, synthetic a posteriori, meaningful, and skeptic—for discipline-based education. The first three types follow the Kantian epistemology described in section 2.1 and form in the cognitive and real worlds, whereas the last two types of knowledge form in the pragmatic world where the preferences of the knowledge formulator and the purposes of applying knowledge become the main ingredients of knowledge. Nonetheless, the definitions of knowledge in Carson (2004), Kinchin et al. (2019), and Ullah (2019) are somewhat informal—defined linguistically, only.
In information science, the concept of knowledge has always existed along with the concepts of information and data, and these three concepts have been used interchangeably. These concepts have started to play an explicit role in engineering problem solving when several machine-learning approaches have been introduced (Quinlan, 1979; Hayes-Rotb et al., 1983; Quinlan, 1986; Nagao, 1990; Heckerman et al., 1995; Studer et al., 1998; Rathman et al., 2017) to facilitate learning from a given dataset. This enables expert systems to solve domain-specific problems (Hayes-Rotb et al., 1983). At the core of these systems lie certain rules (e.g., if…then… rules) extracted from a given dataset using probabilistic reasoning and fuzzy logic (Zadeh, 2002; Ullah and Harib, 2008). Therefore, in information science, machine-learning-enabled rules have been playing the role of knowledge. A few authors in information science have formally defined knowledge with regard to data and information. But these definitions suffer circularity. For example, consider the definitions reported in Nagao (1990) and Mizzaro (2001). Nagao (1990) has classified knowledge into two types—factual and inference-based. Factual knowledge is obtained objectivity, accepted widely, and can be expressed as a sentence or symbolic equation, wherein each term is clearly defined. To represent factual knowledge, other concepts (referred to as primary and secondary information) (Nagao, 1990) can be used as semantic annotations for ease of digital-media-based information processing. On the other hand, using inferences (deductive, inductive, or probabilistic) (Ullah et al., 2012), cognitive reasoning (analogical, common sense, and qualitative), and heuristics, new knowledge can be acquired from factual knowledge. Such knowledge is called inference knowledge (Nagao, 1990, p.9-16). Mizzaro (2001) has introduced a concept called knowledge-state to draw demarcation lines among knowledge, information, and data. Although the temporal nature of a knowledge-state has been studied (Mizzaro, 2001), the types of knowledge have not been shown explicitly in terms of knowledge state or data/information.
3. Revised Definition of Knowledge
The previous section describes the multiplicity and circularity underlying the definitions of knowledge as elaborately as possible referring to different fields (epistemology, engineering design, manufacturing, organization science, education science, and information science). This section presents a revised definition of knowledge from the perspective of Industry 4.0. Before presenting the revised definition, the following points are highlighted for the sake of better understanding.
Despite the multiplicity and circularity in the definitions of knowledge as described in the previous section, there are some common grounds. One of the noteworthy common grounds is related to the representation of knowledge—knowledge graphs (e.g., concept maps) can represent knowledge irrespective of its types, human-learning, machine-learning, and academic fields. To understand this, recall information and education sciences, as described above. In information science, where machine-learning is the main concern, knowledge representation boils down to concept mapping. For example, the types of knowledge called factual and inference knowledge (Nagao, 1990) are represented using semantic networks of concepts wherein the logical operations (AND, OR, NOT, and alike) connect the relevant concepts manifesting a set of if…then… rules (Nagao, 1990). In education science, where human-learning is the main concern, knowledge representation also boils down to concept mapping. For example, Kinchin et al. (2019) and Ullah (2019) represented various types of knowledge using concept maps. A concept map here is a personalized ontology of human understanding regarding a given issue (Ausubel et al., 1978; Ausubel, 2000; Novak, 2002). These types of maps ultimately refer to the assimilation hypothesis of human learning (Seel, 2012). The remarkable thing is that the technology of semantic web wherein the machine and human readable knowledge and linked data will reside for using them in Industry 4.0 will rely on the knowledge graph-based data-format (i.e., concept mapping) (Kim, 2017; Fionda et al., 2019).
On the other hand, there are some disputed grounds. For example, consider the role of logical operations in knowledge formation. There is a split in this regard. To understand this, recall the recall information and organization sciences, as described above. Information science demands application of logical operations for knowledge formulation and transformation, e.g., factual–inference knowledge transformations require sophisticated logical operations (Nagao, 1990). Organization science demands application of social interactions—tacit–explicit knowledge transformations require social interactions only (Nonaka, 1994)).
The demarcation lines among knowledge, data, and information have been an important issue. Though knowledge-state (Mizzaro, 2011) may be used to solve this problem but without the types of knowledge it would be difficult to implement it in real-life scenarios. Though the semantic networks of linked data and knowledge (i.e., knowledge graphs or concept maps) integrate the relevant data, information, and knowledge to solve problems in engineered systems (Mordecai and Dori et al., 2017; Mordecai, 2019; Chen et al., 2019), the maps are created without making any distinctions among knowledge, data, and information. As a result, when the so-called knowledge state (Mizzaro, 2011) changes, its influence randomly propagates to whole network. This means that what has been affected (knowledge, data, or information) to what extend remains obscure. On the other hand, human-learning described so far is based on the assimilation hypothesis (Seel, 2012), which states that without existing knowledge, nothing new can be learned. This contradicts the concept of new knowledge given by the C–K theory of design (Ullah, 2012), and is not desirable because new knowledge is the primary ingredient for creating artifacts. Thus, education-science-driven definitions of knowledge tend to ignore the main ingredient of creativity. On the other hand, which segment of a knowledge graph or concept map is knowledge and which part is not must be known beforehand. This is not possible until a clear demarcation line exist between knowledge and other relevant contents (e.g., data and information). At the same time, the relevant system must aware of the co-existence of different types of knowledge. This is drawback of the exiting knowledge and data prospection using semantic web (Kim, 2017; Fionda et al., 2019).
Based on the abovementioned considerations, this section presents an Industry 4.0- and semantic web-friendly definition of knowledge, which is free from circularity and ambiguity.
The proposed definition of knowledge is as follows. A piece of knowledge (denoted as K ) comprises three elements—knowledge claim (Kclm ), knowledge provenance (Kprv ), and knowledge inference (Kinf ). In general, these elements demonstrate the following relationship.
\(K=\left\{K_{\text{clm}},\ K_{\text{prv}},K_{\inf}\right\}\text{\ \ \ \ \ }K_{\text{prv}}K_{\text{clm}}\)(1)
In the above expression, Kclm denotes a manifestation of K ; that is, Kclmrepresents a piece of knowledge, such as a proposition, an equation, or any other piece of information. Other elements may or may not be reported explicitly; that is, Kprv andKinf may remain empty, butKclm ≠ ∅. Kprv helps identify the truthiness of Kclm . There exist no restrictions that Kclm must be “completely true” or “completely false.” Partially true or partially falseKclm can be used to manifest K . This implies that Kprv may not fully justifyKclm . Kinf refers to the inferential process involved in gaining Kclm in the presence of Kprv . In addition,Kinf helps categorize K into different types and categories. In some cases, Kprv andKinf may remain empty; i.e.,Kprv , Kinf = ∅ is allowed.
The definition of knowledge given by equation (1) yields four fundamental knowledge types—(1) definitional; (2) deductive; (3) inductive; and (4) creative—which are summarized in Table 1 along with their main characteristics and descriptions.
Table 1. Definition of Knowledge