Knowledge Management and Modelling

Knowledge management considers the interrelated themes of people, process and technology and how they relate to the implementation of knowledge management systems (Pope and Butler, 2012). Knowledge is commonly acquired by people either though communicating with experts who already hold the knowledge or by accessing written knowledge stored in a knowledge repository (Milton, 2007). Written or explicit knowledge is made available in a range of formats including books, electronic documents, websites, videos or specialist knowledge representation tools. Semantic models such as glossaries, concept maps and ontologies are example of tools used for the processes making knowledge explicit also referred to as knowledge modelling. Knowledge management takes place in the context of human organisations that have procedures that support and constrain knowledge sharing between individuals. These processes create tacit and explicit knowledge through an iterative cycle of socialisation, externalisation, combination and internalisation that involves interactions between humans and knowledge (Nonaka et al., 2000). This is similar to process of semantic modelling which involves a modeller eliciting domain knowledge from a domain expert for representation in an ontology or logical data model.

The most significant barrier to knowledge management is the changing of organisational culture to make the sharing of knowledge the norm (Blair, 2002). Such a culture is a prerequisite for effective communicated between subject matter experts and knowledge modellers or business analysts. The acquisition of knowledge is

supported by a range of technologies including websites, blogs, document management systems and knowledge modelling tools. Knowledge management projects that primarily focus on technical implementations often fail as the technology becomes an end onto itself rather than a solution that successfully support knowledge sharing. Knowledge modelling discussions frequently begin the question of ‘What is knowledge’ followed by a discussion that contrasts data, information and knowledge. Knowledge is described as either the conceptual understanding acquired by a person as they refine information (Rowley, 2007), or as the procedures that a person follows to correctly apply information in a given context (Gurteen, 1999). Milton (2007) suggests that knowledge can be categorised using the dimensions of explicit vs. tacit and conceptual vs. procedural. This approach is used in Table 3.1 to categorise examples of knowledge a person might have about bank customers. Each type of knowledge has associated types of representation or models. The types of semantic models discussed in Chapter 2 are associated with representing explicit conceptual knowledge.

Table 3.1 : Models or Representation of Types of Knowledge

Explicit Knowledge

Gained though reading or education and not difficult to write down and explain

Tacit Knowledge

Gained through practice or personal experience and difficult to explain and write down

Conceptual Knowledge

Concepts and how they are related to each other

Models: Taxonomy, Concept Map, Ontology

Example: ‘I know that the bank has customers and that each one has a credit rating’

Models: Beliefs & Biases, Mental Models

Example: ‘I know that some customers in this branch manipulate their credit rating’

Procedural Knowledge

How to perform steps to complete a task

Models: Process Map, Algorithm, Demonstration Video

Example: ‘I know to calculate the credit rating for a customer’

Models: Heuristic, Intuition Example: ‘The way this customer is applying for their loan makes me think they are up to something’

The process of both semantic and knowledge modelling involves humans interacting with other humans in order to produce an explicit representation of knowledge. Knowledge modelling is part of the iterative knowledge creation processes described by Nonaka et al. (2000) as the SECI cycle, which is illustrated in Figure 3.1.

Figure 3.1 : The SECI Process (Nonaka et al., 2000)

The four stages of the SECI process can related to semantic modelling tasks as follows;

1. Socialisation: Tacit knowledge is shared between modellers and domain experts though natural techniques such as conversations, interviews and workshops. New tacit knowledge is created as experiences are shared.

2. Externalisation: Modellers articulate their tacit knowledge as an explicit model using a specific modelling technique. The new explicit knowledge is shared requested from other modellers and domain experts for comment and feedback. 3. Combination: The modeller combines the new semantic model with previously

developed complex models. This identifies concepts that are common between the models and also identifies new relationships. This may result in re-appraisal and rework as the modeller follows the structural rules required of the modelling technique.

4. Internalisation: The act of modelling is ‘learning by doing’ for both the modeller and domain expert. New tacit knowledge of the domain being modelled is acquired by all participants in the modelling process. The new explicit model is read by others so that they can use it to acquire new knowledge. This results in further socialisation of the concepts which starts the cycle again.

Correndo and Alani (2007) describe how an ontology based knowledge repository can be created by communities of knowledge workers using semantic web technologies and collaborative knowledge construction techniques For large groups of users who are not co-located it is a requirement that the ontology tools support both remote and continuous participation by all members of the group. The required features of the tools for knowledge repository creation identified by Correndo and Alani can be mapped to the SECI process as follows;

 Socialisation: Support for discussion and consensus building between users such as instant messaging, discussion with annotations and voting or rating of concepts.

 Externalisation: Distributed editing functionality taxonomy editor that is available in real time to other users, common space for modelling

 Combination: Searching for existing terms to maximising reuse, tagging to help reuse, consistency checks where merging ontologies

 Internalisation: Visualisation tools to browse the ontology, Notes to provide advice on use of concepts