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Daily Knowledge

From the beginning, knowledge has been a preoccupation for humans. A lot of questions are still being discussed: What is knowledge? How is knowledge built? How is it represented in the mind? How can it be kept? How can it be learned? etc.

In this book, we deal with the notion of daily knowledge. We try to answer questions that have been discussed before. However, first of all, let us present the notion of knowledge as it is discussed in the knowledge engineering community. We also talk about individual versus collective knowledge to conclude by showing how we consider daily knowledge and challenges to deal with in order to manage daily knowledge.

1.1. Knowledge

The notion of knowledge has been defined since Antiquity. Plato, for instance, defined thought as the intellectual model of objects. Heraclite went toward the definition of the logos as a triangle which distinguished thought, from expression, from reality. Saussure in his course [SAU 83] defined the base of the semiotic: a representation of knowledge embedded in an activity is related to a specific symbol. Currently, these representations are increasingly used to enhance learning from expertise and past experience. So, a human has to recognize concepts in the reference to make sense.

A sense is the combination of a signifier (the form which the sign takes) and a signified (the concept it represents). Within this theory, humans identify a sign from both the signifier and the signified. The semiotic triangle completed the representation of this theory with the use of three dimensions of knowledge: “sense”, “referee” and “symbol”. A human gives a sense to a symbol based on his/her referee (Figure 1.1).

Figure 1.1. Semiotic triangle

The opening of computer science to cognitive psychology in the 1950s, especially launched by the conference at Dartmouth in 1956, promoted the first analysis of how to represent human knowledge in a computational way. The first artificial intelligence studies concerned the development of an expert system (for instance the MYCIN system in the 1970s), in which expert knowledge is represented. The notion of the expert system became knowledge-based systems in the 1990s. A number of researchers studied how to represent knowledge based on logic. Thus, semantic networks and frames are defined with this aim [BRA 92]. Conceptual languages are also defined based on these theories. We mainly note conceptual graphs [SOW 14] and conceptual modeling language [SCH 94]. These studies are the basic principles of the current knowledge engineering theories in from which several techniques and notions took root: “conceptual models” of an expertise [BRE 94, AUS 94] and “ontologies” [FEN 01, BAC 00, CHA 04, KAS 02, KAS 05, GUA 98]. In these types of theories, knowledge is extracted from expert documents and by interviewing experts and represented in a conceptual model. This conceptual model can then be implemented using logics. Some methods such as MASK [ERM 00] use the conceptual representation to enhance learning between actors in an organization. Ermine in his method uses schematic forms in order to show links between concepts. He mentions the knowledge system and adds the representation of the context (borrowed from systemic science) to show different views of knowledge (Figure 1.2). His methods are largely used not in knowledge engineering but in knowledge management.

Figure 1.2. MASK views to represent knowledge. For a color version of the figure, see www.iste.co.uk/matta/knowledge.zip

The notion of knowledge management on the other hand began in management science in the 1990s [GRU 00, NON 95]. Knowledge management is the challenge of promoting the valorization of knowledge in an organization as a product. Some work in management science and economies goes beyond this and declares knowledge as the corner stone of a company [EVA 13, POL 66]. Polyani and Nonaka and Takeushi mentioned the notion of explicit and tacit knowledge. Nonaka and Takeushi defined the principle of transformation of knowledge between tacit and explicit knowledge (Figure 1.3).

Figure 1.3. SECI model [NON 95] . For a color version of the figure, see www.iste.co.uk/matta/knowledge.zip

The challenge of this type of work is how to support knowledge transformation in a company. So, we observe a number of techniques for this aim, for instance, the community of practices [LEV 97] and knowledge capitalization [DIE 02] approaches. The notion of corporate memory is defined as explicit and disembodied knowledge in a company [DIE 02]. Several techniques are inherited from knowledge engineering in order to enhance knowledge extraction in a company. These techniques have been adapted and completed. We note especially the MASK [ERM 00, MAT 02] and REX methods [MAL 93].

In management science, knowledge is considered as the production of interaction between actors [GRU 00]. To tackle this knowledge, techniques have to enhance this interaction. Grundstein [GRU 00] maintains that a system, which allows us to expertise identification, is better than knowledge extraction. Based on this principle, techniques allowing a knowledge map in a company have been presented [GRU 00, ERM 06, MAH 05].

If we refer to the definition of knowledge as the interaction between sign, reference and sense, a knowledge system must allow these three dimensions to be represented. In a company, actors produce knowledge because they interact continuously with these dimensions that are present in their work environment (interaction with a problem, interaction with other actors, interaction with a situation, etc.). So, managing knowledge in a company leads us to support these interactions. To this aim, some authors maintain that context will be represented with “How” an activity can be done and “What” type of concepts are used [COL 98]. Even current work in ontologies [CHA 04, KAS 02, KAS 05] promotes links between concepts and the documents from which they are produced. A lot of work presents tools enhancing document tagging [BEN 01, BEN 09] and concept construction [ZAC 07] in order to keep links between concepts and context.

In this book, we deal with knowledge as the interaction between an actor and his/her work environment. We study this interaction in cooperative activity in daily work. So, our main goal is to define techniques that help to enhance collaborative knowledge.

1.2. Daily knowledge

Daily knowledge consists mainly of know-how produced in daily work by a human. In the study by Richard [RIC 90], daily knowledge is considered as episodic memory, which contributes to build epistemic knowledge (or deep knowledge, as we mention in knowledge engineering). So, daily knowledge is dependent on the context in which it is produced (activity, environments, tools, etc.). Representing this type of knowledge also leads to representing its context and, especially, the organization and the environment in which it is produced.

Related to this postulate, the generation of a sense as it is represented in the semiotic triangle (Figure 1.1) cannot be done without the recognition of the context, which led to producing the reference. This postulate is further verified when we believe that knowledge is produced by the interaction of an actor with his/her environment. So the challenge is how to capture the context of the production of knowledge and how to represent it in order to enhance the generation of sense when learning from this knowledge (Figure 1.4). “The learning content is context specific, and it implies discovery of what is to be done when and how according to the specific organizations routines” [EAS 07].

Figure 1.4. Enhancing daily knowledge

So the main challenge is how to manage daily knowledge? How can we keep track of it by considering all the elements of the environment that contribute to its production: interaction, organization, roles, tasks, constraints, rules, means, methods, goals, products, artifacts, etc.

Currently, in organizations, collaborative activities are becoming more and more present. Dealing with the complexity of problems, actors have to solve problems in a collaborative way by interacting with other actors. So, we believe that observing daily knowledge production leads to dealing with collaborative activities. In this book, we study knowledge produced in collaborative activities, which we call “collaborative knowledge”.

1.3. Individual versus collaborative knowledge

As noted above, we deal with knowledge as the interaction between an actor and his/her environment. Some approaches in knowledge management study how to represent individual knowledge while others help to enhance collaborative knowledge. Before detailing our study on the management of collaborative knowledge from daily work, let us discuss the difference between individual and collaborative knowledge (Figure 1.5).

Figure 1.5. Difference between individual and collaborative knowledge

1.3.1. Difference in the nature of captured knowledge

In fact, knowledge capitalized with knowledge engineering approaches is related to experience. This experience is built along the activities of an expert in which a lot of experiments are analyzed and structured by the expert; knowledge engineering approaches are based on the cognitive...