Journal of Knowledge Management Practice, Vol. 8, No. 3, September 2007

An Ontology Based Knowledge Experiential Learning Framework

Khalid Samara, Dilip Patel, Shushma Patel, London South Bank University


Ontologies are no longer just a means of relating or unfolding semantic operations. Rather an ontology is firmly contained within the virtual world where technologies have unraveled its full semantic potentials. The contribution of knowledge management (KM) combined with different software applications calls for increasing pedagogical support for extending both ontology and the knowledge management environment. Though there are multiple KM concerns within health care, medicine is a domain which has the potential to show how good ontology can yield demonstrable benefits in human interests. This paper presents a framework incorporating the learning processes, knowledge base characteristics, and ontology applications for a clinical environment. This type of integration leads to increasing the understanding of ontology applications, collaboration, and competence.

Keywords: Knowledge Management, Ontology, Experiential Learning, Clinical Practice, ICT, Tacit and Explicit Knowledge

1. Introduction

This paper postulates that the full potential benefit of clinical knowledge area is a prerequisite to changing practice. The ontology approach have been recognised as the next step in the evolution as a distributed knowledge source. Ontology’s provide the framework infrastructure for semantic interoperability and addressing heterogeneous knowledge sources and generating knowledge fusion. Unfortunately, because of the autonomous nature of the clinician and the organisation incorporating the use of knowledge management ontology’s may prove to be challenging. Furthermore, pedagogical activities are often not included into the ways new technologies are utilised and as a result engaging in these systems has not always resulted in the learning outcomes that were promised (Kay and Dyson, 2006).

The gap between KM and experiential learning would imply that methods that rely solely on increasing clinician knowledge through the passive transfer of unsolicited and didactic information are unlikely to have a substantial impact on clinical practice. What’s more as these knowledge sources evolve the barrier to translate specific research evidence into clinical practice is unpredictable and inconsistent. In order to develop guidelines for ontology’s to support knowledge sharing and creation we need a careful analysis of human and organisational learning. Therefore, to bridge this void the paper provides a theoretical frame paying special attention in the use of experiential learning and ontology to support collaboration for the synthesis of heterogeneous knowledge sources. Also as learning needs to be integrated central to the framework is the design and development of facilitating new learning services a shift from current oriented learning towards a collaborative paradigm.

2. Knowledge Management And Learning Barriers

In order to understand how human actions can provide the ontological bond that binds organisations together we need to amplify on some central features of experiential learning that often goes unnoticed. Also knowledge and learning are personally connected in the sense that knowledge is the product of learning. In recent time context oriented learning has been multifaceted by way of semantically expressing relations among different types of learning objects in the context of use in learning surroundings (Knight et al., 2006). Learning objects is a type of knowledge object that area self-reliant and reusable, its aim is to present knowledge in support of related learning objectives (Cohen and Nycz 2006).

Though KM offers new insights into learning contents, it is continually engaged in complex and dynamical environments constrained by socio-technological factors and the major disregard to experiential learning has widened this void. The problems with the current KM are the inability to support the fusion of heterogeneous knowledge sources. Gualtieri and Ruffolo (2005) argue that to satisfy these requirements KM systems needs knowledge representation capabilities supporting interoperability among different systems. Ruta et al., (2005) recommends that the tacit knowledge borne of individual clinical experience is invaluable in articulating the criteria for assessing practitioners to discover and assess subjectively the benefits gained or lost. These findings confirm that the resulting problems isolate the fusion and development of personal knowledge adding even more heterogeneity in the workplace and building a paradox with the specification rationale of the KM systems, which supports both tacit and explicit attributes of knowledge.

In definition knowledge is the product of learning a psychological result of perception, learning and reasoning (Cohen and Nycz 2006). The two types of knowledge are tacit and explicit knowledge as supported by (Nonaka 1994; Polanyi 1966; Ramesh and Tiwana, 1999). Other researchers have identified disciplines and processes that seem to be associated with building a learning organisation (Senge 1990; Nonaka and Kenney 1991). The concept of network organisation derived from learning organisation also described as the externalisation of knowledge from tacit to explicit taking place in a peer-to-peer setting (Nonaka and Takeuchi, 1995). However, the difficulty even in the field of organisational learning involves the eliciting of tacit knowledge, which has been expressed as the knowledge engineering paradox (Vizenor, 2004). Such as the more knowledge a clinician has, the harder it is to extract and articulate into a repository. What’s more, Needlman (2006) proposes that primary care clinicians need to focus on experiential knowledge in practice as apposed to didactic learning. This way primary care recommendation would be based on evidence instead of just personal judgements (Needlman, 2006).

Shaw et al., (2004) states that the relationships within primary care organisations must extend towards a learning organisation with external organisations and individuals to gain necessary new knowledge and skills. Sheaff and Pilgrim (2006) argue that a learning organisation attempts to formalise the tacit knowledge that production teams rely on. Nonaka (1994) for instance, maintains that the driving force in the process of competency accumulation is the individual. Nonaka and Takeuchi (1995) believe that what’s needed is a people centric approach, from bottom-up, but managed and supervised by top management. However, Sheu and Wong (2006) suggests that the learning dimensions are vital for the nurturing of KM and the focus on developing new knowledge should be based on knowledge structures rather than collecting isolated knowledge entities.

These knowledge structures are identified as knowledge representations facilitating understanding and knowledge acquisition by directing learning, this structural learning and knowledge sharing is represented by applying soft systems methodologies (Sheu and Wong 2006). Sheu and Wong (2006) call this conceptual learning through learning by analogy. The conceptual methodology approach facilitates the opens systems concept in which people in the learning environment become consistent with systems thinking, the organisational goals and strategies must be linked to KM. Sheaff and Pilgrim (2006) argue that the primary health care has been criticised for its lack of cohesive structuring instead primary care is fragmented made up of decision makers and this is also reflected by lack of leadership. Nonaka and Takeuchi (1995) claim that a successful knowledge management is a concomitant or implication of a learning organisation, also is said to increase dynamic capability. This view is also held by Ramesh and Tiwana (1999) that knowledge about the process of product development is unfinished in the beginning and expands gradually over time, through various modes of learning. However, Clergeau (2005) also illustrates that knowledge is deeply embodied in the organisation especially in the information systems and less dependent on the individual. This occurrence are analysed as an adaptation of the basis of the organisations fundamental competencies from one dependent on the individual and other on information systems (Clergeau, 2005). This partitioning impinges on the learning processes hence the firm may have to re-equilibrate the tacit component of the individual and the codified collective components (Clergeau, 2005).

3. Ontology And Experiential Learning

Ontology’s and KM are normally associated with groupware technologies, a set of semantic web based tools which support communication, collaboration and coordination, also play an important enabling role for learning organisation and for engendering new knowledge. The resource description framework (RDF) and XML are the current standards for establishing semantic interoperability on the Web. Web Ontology Language (OWL) designed by the W3C Web Ontology Working Group to provide a language used for applications that need to understand the logical content of information. The OWL language can be used to allow the explicit representation of term vocabularies and the relationships between entities in these vocabularies. Figure 1 is an example of a hierarchical logic knowledge base that consists of a set of axioms asserting or semantic interconnections e.g., that one class is a subclass of another, or that Cardio is an instance (objects) of Heart class. Therefore, a knowledge base contains an axiom stating that Heart contains subclasses such as type of diseases, and the meaning of a knowledge base is obtained from characteristics and relationships that are universal to all potential models.

The ontology approach has the base for controlling the terminological inconsistency in expression through generic building of concepts applying the dedicated tools to convert for instance medical language into semantically meaningful representations. Generic concepts can be leveraged through a learning environment forming a knowledge domain through specific learning objective. The problem with knowledge, are that they are difficult to accumulate for each individual, except the ways in which virtual learning is conducted knowledge begin to evolve. However, one of the strengths in applying experiential learning is the application of knowledge in a hands-on approach. Specifically, the ontological tools available i.e. RDF - OWL offer a venue for problem solving in a real-life situation without clinical risk or time constraints giving the clinician and patients the opportunity to focus their problem-solving abilities. All the same, while ontology’s begin to evolve more and more into the realms of technology and into the virtual domain it is necessary to begin to form the transferability of their learning to other settings and situations.

In a primary health care setting if a virtual ontological environment becomes a reality, all the knowledge within it would include in creating and maintaining a large data warehouse thereby reducing heterogeneity. Such as in the case of clinical guidelines, which are a set of plans assisting clinicians in decision making about appropriate health care. Kiessling and Henriksson (2002) suggest that more is required than just distributing practice guidelines to increase knowledge and change the outlook and performance of clinicians. Clinical guidelines are a major tool in improving the quality of medical care. Furthermore, the context of a consultation in daily clinical practice is unstructured, unlike the structured situation when a patient is registered in a clinical trial (Kiessling and Henriksson, 2002).

Though the health care is moving towards a digital health care system, ultimately virtual tools must be designed and built to integrate knowledge into systems care. Instead the lack of cohesive structuring and the inclination of multifaceted decision makers have made health care learning environment problematic. Learning and ontology is explored in Loos (2006) states that an essential part of learning is the human combined activity. Learning just makes sense when learning happens on demand and more activities occurs rather than passivity (Loos, 2006) However, to appreciate the various types of ontology associated technologies we need to understand the psychological needs of the learner (Cohen and Nycz 2006). What stimulates the learner, however, is not necessarily the virtual settings but the activities that the ontology environment brings with it, and the interactive group processes of group building that generates the demand learning.

4. Ontology And The Technology Paradox

The core of the problem is that the current core knowledge culture in the health care is the collectively reinforced, internalised, tacit guidelines experienced between practitioners within their domain (Gabbay and Le May 2004). This knowledge must be made explicit during the conversion process. A clinician who has learnt in this way may well have various rules of thumb or oversimplifications about what to do in different situations. Though multiple dimensions in an organisation can amplify the type of knowledge culture it is not simply a conversion process from machine executable representations as these can be put right if consistency is applied. However, the challenge is how to create the “tutor machine environment” necessary for the evolutionary process to utilise the technology needed for conversion. However, Kay and Dyson (2006) argue that experiential learning is found to be less common in online learning environments. Clergeau (2005) claims that information and communication technologies (ICT) are responsible for a serious decrease in the relative value of human capital. Paul (2006) found that ICT does not facilitate in communicating emotions, intuition, and context that are crucial part for such narratives. Until now ICT have presented a dilemma in effective collaborative activities; ICTs are more appropriate for highly codified knowledge and less suited for the transfer of tacit knowledge (Paul, 2006). The implications are that ICTs do not adequately support the use of narratives through which tacit knowledge is often communicated (Paul, 2006).

Gualtieri and Ruffolo (2005) argue that the current information systems present two basic problems; firstly they are able to process only a small portion of the whole organisations knowledge; secondly they use heterogeneous models and techniques for representing knowledge and manipulating them. Ghosh and Scott (2005) also identify that there are two types of knowledge processing which are interactive and integrative. Gosh and Scott (2005) emphasise that KM systems is a by-product of interaction and collaboration rather than the primary focus of the application and that KM processing is integrative and not so much focus is placed on the interaction of the contributors, as on the integrating, the explicit knowledge which resides in a KM system. However, what’s common to most problems is that since clinicians cannot program and rarely use technologies during practice it is necessary that they do interact at some point. The paper advocates that the collective pedagogical and ontological activities allows the knowledge engineer and the clinician to become facilitated into a collaborative environment, to inherit and support an iterative educational and learning process.

Figure 2 presents a framework for integrating ontology driven experiential learning to increase understanding of ontology applications to apply knowledge, skills in an immediate and relevant setting. Unlike most applications ontology’s inherent an inimitable knowledge transformation experience which results from a combination of portability and semantic interconnections. The ‘application’, described in this paper is a system or process, (usually computer systems and knowledge base systems) which makes use of or benefits from the ontology. The dependencies emphasise that experience plays in the learning process as a holistic framework assembled by a number of systems. The clinician can learn about KM while sharing and creating knowledge in a computer supported or virtual environment. The framework attempts to capture the knowledge base characteristics and views that are helpful in the learning process. Also what is required is to leverage or to translate tacit into explicit knowledge throughout the collaborative learning process to yield a comprehensive knowledge resource for decision-support. If learning has taken place the process could be seen as a spiral action. The action is taking place in a diverse set of conditions and the learner (clinician) is now able to expect the possible effects of the action.















Figure 2 An Ontology Based Knowledge Learning Framework

Ontology Driven Experiential Learning:

      Identify learning relationships and associate them with appropriate applications.

      Create experience via observation and reflection.

      Consider building ontology’s consistently with other systems or applications.

Experiential Learning For Designing And Building A Learning Environment:

      Begin to establish collaboration.

      Synthesising findings and making recommendation about the best way for knowledge and learning initiatives.

Ontology And Experiential Learning Focus On Knowledge Sharing And Storage:

      A dynamic is established between the processes of experiential learning and ontology’s through experience engaged in learning progression.

      Identify how clinicians learn and begin to build demand learning.

      Extend learning through tools to increase an effective, productive system within an organisation.

      Tools can impact on knowledge management for information collection, collaboration and communication.

Clinicians Engage In Knowledge Sharing Through Objective Learning:

      Disallow subjective experiences in the process of behavioural learning.

      Finding and making recommendations about the best way forward for knowledge and learning initiatives.

Tacit Knowledge Is Captured And Made Explicit Through Learning Objects:

      Mapping result in a virtual integration.

      Tacit knowledge is built through the combination of grasping and transforming experiences.

      Manage the conversion of tacit into explicit knowledge by applying collaboration, experiential learning and tools.

5. Conclusion

Experiential learning recognises that the new realities of ontology applications have a significant impact on knowledge management environment within organisations. Though pedagogical activities are normally an absent practice they now require greater attention than ever before due to the importance and increasing frequency of changes to the organisational operations, the knowledge-culture, the growing range of information and applications used. The framework presents and supports the fusion of pedagogical activities and knowledge-conversion for an ontology based user environment. The unstructured context of consultation in daily clinical practice makes ontology and experiential learning intervention desirable. The challenges faced by clinicians engaged in the rules of thumb culture yet highlights the problems with transforming tacit into explicit knowledge it is hoped that ontology applications and learning actions will bring to the attention of healthcare and researchers the potential of ontology based knowledge experiential learning activities.

6. References

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Contact the Authors:

Khalid Samara, Phd Student, Centre For Information Management and E-Business, Room 330, London South Bank University, London SE1 OAA

Professor Shushma Patel, Professor of Information Systems, Faculty of Business, Computing & Information Management, London South Bank University, London SE1

Professor Dilip Patel, Professor of Information Systems, Faculty of Business, Computing and Information Management, London South Bank University, London SE1