Enterprise Ontologies and Knowledge Management

7th International Conference on Concurrent Enterprising 27-29 June 2001, Bremen

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Enterprise Ontologies and Knowledge Management Jan Goossenaerts, Christine Pelletier Eindhoven University of Technology, Faculty of Technology Management, POBox 513, 5600 MB Eindhoven, The Netherlands {J.B.M.Goossenaerts,C.P.M.Pelletier}@tm.tue.nl Abstract This paper analyses the contribution to knowledge management of an enterprise reference architecture and enterprise ontologies constructed in accordance with it. A particular enterprise ontology has to support the knowledge acquisition, discovery and modeling efforts of the endusers within the context of their enterprise including the software applications (e.g., ERP, MES, Socio-Technical Tool, Ergonomy Tools) deployed in the enterprise. State of the art in knowledge management; the rapid developments and increasing use made of IT in enterprise operations, calls for increasing IT support also during the knowledge intensive life cycle phases of enterprise entities. The role of enterprise ontologies for knowledge creation in organisations is explained by reference to the life-cycle phases of enterprise entities as defined in GERAM. Section 3 explains an ontology based model for knowledge exchange among different software applications that are used during the knowledge creating processes. This model applies the dimension of genericity of the ENV 40003 reference architecture: generic constructs and models are specialized to construct partial models, partial models are further specialized, instantiated and aggregated to serve one particular enterprise domain. Keywords Knowledge exchange, enterprise modelling, ontology

1

Introduction

This paper contributes some mid-project results of the European component PSIM of the IMS/RTD project “Organizational Aspects of Human-Machine Coexisting Systems” (HUMACS). PSIM is developing and pilot-demonstrating a Participative Simulation environment for Integral (i.e., involving logistics, technology and human factors) Manufacturing enterprise renewal. Although PSIM itself is focused on assisting human creativity and the application of ICT technology within assembly organisations [Berg, Eijnatten, Vink, Goossenaerts, 1999], the concepts and principles applied are transferable to concurrent enterprising as well. Seen from the perspective of knowledge management, the key contributions of PSIM are related to the development of novel IT supported approaches to amplify knowledge in the upward spiral knowledge-creating process: from the individual, over the team to the organisation. The PSIM environment aims to enable the knowledge processing to make the work methods of a manufacturing enterprise reflect all manufacturing expertise that is available in the organisation. As the organisation’s processes are increasingly been supported by software tools and applications, the four types of knowledge conversion of the SECI model: socialisation, externalisation, combination, and internalisation [Nonaka, 1991] will also involve these software’s data and processing. The enterprise’s IT environment has become both a key carrier of explicit knowledge and a hurdle in the amplifying spiral, with the IT professional as a new indispensable role in the upward spiral knowledge-creating process. Participation is the process that allows employees to exert some influence over their work and the conditions under which they work. Competence and capability are "both a requirement for

Knowledge Management

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and a consequence of participation" [Heller, Pusic, Strauss, Wilpert, 1998, p.45]; a requirement because participation needs a minimum level of skills in order to be effective, and a consequence because participation enhances the skills levels of those involved. Participation assumes a smooth mutual communication between management and employees. Given the new IT realities, interdisciplinary participative reflection in the design of new systems, or the reconfiguration of existing ones, must be supported also by dedicated IT support. Simulation is the construction and use of a computer-based representation, or model, of some part of the real world as a substitute vehicle for experiment and behaviour prediction. Simulation offers an attractive opportunity for engineers, planners, managers and teams to try out ideas in advance of commitment to a course of action [Groumpos, Krauth, 1997]. The goal of participative simulation is to enable workers to exert direct influence over the product and process designs by bringing in their tacit knowledge, to combine it with expert knowledge, and to put the blend of both insights to the test, and eventually into practice. When these experimenting, problem solving and implementing activities are supported by highly visual representations of abstract processes, that contribute to a common basis for discussions and suggestions, the resulting continuous improvement process may become even more intrinsically motivating for the work force [Berg, Eijnatten, Vink, Goossenaerts, 1999]. The stand-alone nature of most simulation tools, their lack of support for an integrated perspective on possible changes to reality, and the poor connectivity of tools to enacted models and real life data, all illustrate the problematic position of software in organisational knowledge creation. The PSIM participative simulation environment aims to (re-)enhance organisational knowledge creation by accomplishing that several tools and several content bases can be combined with the transparency of “a single multi-user tool”. Enterprise ontologies, a navigator of “explicit knowledge”, and integration services are the main components of the PSIM Environment. Section 2 presents some general insights on the role of enterprise ontologies for knowledge creation in organisations. Section 3 explains an ontology based model for knowledge exchange among different software applications. This is one of the mid-project results of PSIM. The navigator and procedures that people should follow when using the PSIM environment are the object of ongoing development. Pilot demonstrations are being prepared as well. Section 4 draws some conclusions and indicates areas for transferring PSIM approaches to concurrent enterprising.

2 2.1

Ontologies for the Knowledge-Creating Organisation Ontology

An ontology corresponds in practice to a set of formal terms, usually with a hierarchical organisation, with associated formal definitions that specify their relationships with the other formal terms, and a set of constraints about their use in the knowledge representation of the domain studied. Each term can be seen as a knowledge category that can be instantiated. General guidelines have been stated to help the ontology builders in their task [Uschold, King, Moralee, Zorgios, 1998]. An ontology has to be rooted in a broad mutual understanding and agreement of the different domain stakeholders. The stakeholders have to agree on the general hypotheses made on the knowledge domain. These hypotheses deal with the different high level categories or concepts, which are used to express the objects or object types and their relations [Guarino, Carrara, Giaretta,1994]. For the hypotheses and mutual understanding in enterprise integration and open systems the PSIM ontologies have built on existing knowledge derived from CIMOSA [AMICE, 1993] and ENV 40 003 [1990] and the IFIP-IFAC task force[Williams et al.,1994] which has found -- albeit in a modified way-- wide application in tools, such as ARIS and DEM, supporting the


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implementation of enterprise resource planning systems. The domain hypotheses of these tools are focussed on the information system for repetitive and integrated enterprise operations, the development and deployment of IT tools, especially ERP software packages, and their use of enterprise modelling techniques.

2.2

The Knowledge Creating Organisation

The PSIM approach to knowledge management is based on the idea that the power of knowledge is not so much leveraged by having knowledge. It is far more important to know, how to allocate knowledge for productive use (Drucker, 1991). Nonaka and Takeuchi discuss the role of an organization in allocating knowledge: “The organization supports creative individuals or provides contexts for them to create knowledge. Organizational knowledge creation, therefore, should be understood as a process that ‘organizationally’ amplifies the knowledge created by individuals and crystallizes it as a part of the knowledge network of the organization” [Nonaka, Takeuchi, 1995: 59]. The successful execution of this organizational activity can be regarded as a core competence. According to Nonaka’s SECI model [Nonaka, 1991] knowledge creation is a continuous and dynamic interaction, spiralling circularity, between tacit and explicit knowledge. This interaction is shaped by shifts between different modes of knowledge conversion: from "Socialisation" – workers interacting with one another, as well as experience -- to "Externalisation" – or articulation, knowledge is made explicit, for example in drawings, models, their evaluations, etc. --, to "Combination" – explicit knowledge is converted into more complex sets of explicit knowledge, for example in plans, reports, work instructions-- , to "Internalisation" – the conversion of the organisation’s explicit knowledge into the organisation’s tacit knowledge -and again to socialisation and so on [Nonaka, Takeuchi, 1995]. Moreover the knowledge creation happens continuously and in multiple concurrently operating teams. In figure 1 the A arrow represents the primary process of the team or the organisation, and the B arrow represents the self-transformation. In its self-transformation the organisation abandons knowledge that has become obsolete and learns to create new things through continuing improvement of its activities, the development of new applications from successes, and continuous innovation. The B arrow will apply the SECI modes of knowledge conversion.

A self-transformation

B operation A’

Figure 1: Operation (A) and Self-transformation (B)

The scope of Knowledge Management goes beyond the management of articulated or so-called "explicit" knowledge. Explicit knowledge is knowledge that can be laid down (documented) in various types of representations and can be transformed, elaborated, learned, etc. from these representations. Current discussion on competence-based competition often prefers tacit knowledge as a source of competitive advantage. But whereas tacit knowledge processing is feasible for people, during all modes of knowledge conversion of the SECI model, it is not for the software applications: the pervasive role of IT in the organisation demands explicit knowledge, for instance in the form of one or more enacted enterprise and/or product models.


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Hence, while people internalise newly combined and tested explicit knowledge for execution of a primary process, this same new knowledge may impact plenty of larger and smaller models in various “languages” which are preconditions for the efficient execution of the same primary process.

2.3

Enterprise Ontologies

To support a worker in acting as both an operator (during operation) and a decision maker (during self-transformation), explicit knowledge must be mapped between several parallel and coherent realities corresponding, respectively, to one real and many “virtual” enterprises. •

In the unique real enterprise (Figure 1, arrow A) data evolve according with primary process (object instances are parts, machines, human resources, activities like physical transformations) ,

•

The virtual enterprise data (Figure 1, arrow A’) evolve according with improvement process (arrow B) (objects are the activity models, resource models, etc., which are being developed or evaluated). GERAM [IFIP-IFAC Task Force, 1998] defines the following life-cycle activity types for any enterprise or entity: Identification, Concept, Requirement, Design, Implementation, Operation, Decommission. The life history of an enterprise (or any business entity) is then defined in terms of the tasks carried out on that enterprise. A task may be carried out more than once, and tasks from several phases may be carried out in parallel. For example, multiple change processes may be in effect at any one time, running in parallel to the operation of the entity. Along the life phases of enterprise entities, partial PSIM ontologies have been developed according to the following division: •

The PSIM-ontology (Figure 2) defines concepts for the operative processes, these are all enterprise processes during the Operation phase of their life cycle. For example, the design process, applied to new products and/or manufacturing systems, is an operative process.

•

The PSIM-procedure model (Figure 2) defines concepts for the self-transformation processes, these are all enterprise processes during the Identification, Concept, Requirement, Design, Implementation and Decommission phases of their life cycle. Therefore the redesign and/or optimisation of a manufacturing process is a part of a selftransformation process (improvement or renewal process). In current IT environments, the concepts for operative processes are to a large extend covered by ERP systems. The additional purpose of the PSIM-procedure model is to allow the enactment of systematic practices for the management of self-transformation within an organisation. As this partial ontology is further developed it will allow the enterprise to enact its particular variant of the SECI model, and to benefit from IT support for its self-transformation. The next chapter describes the PSIM environment and the PSIM ontologies in some more detail, and it addresses one application of the ontology, the knowledge exchange between software tools.

3

A Model for Knowledge Exchange

The aim of the PSIM environment is to favour the participation and the sharing of knowledge by supporting multi-dimensional design and redesign analysis of whole or part of the enterprise. Dimensions of analysis could include logistics, technology and human factors. For this purpose the PSIM environment enables the communication between different application tools, each of them providing an answer to a set of enterprise problems according to a precise context


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(financial, organisational, …) and using different methods. Hence, it supports decision in a wider understanding of the problems and of possible solutions, and offers a more complete knowledge about the possible consequences related to each solution on the different point of view [PSIM Deliverable 4.1, 2001]. In the following sections we will first briefly describe the general architecture of the PSIM environment. Then, we will focus on the component supporting formally the communication between the different application tools: the PSIM ontology. Last section will be dedicated to an example of connection between the ergonomic concepts and the PSIM ontology.

3.1

PSIM Environment

To support the exchange of information between application tools and between users and to enable these tools to work together and to share information, the PSIM environment is composed by two major components: the centralized translator and the navigator. •

The centralized translator is composed by three components: the PSIM-ontology, the communication layer and the PSIM-procedure model. The main component is the PSIMontology, which correspond to a common language framework, structured by a generic enterprise ontology, the PSIM meta-ontology. Within the framework defined by the PSIM-ontology, a model of enterprise is embedded. The communication layer is composed of a set of translators (one for each tool embedded in the PSIM-environment). The role of a translator is to ensure the coherence of the exchange of data between tools. In each translator, a local tool concept has a synonym. The PSIM procedure model describes what the user can do within the environment and how s/he can perform it. This model uses the enterprise model defined in the PSIM ontology and is structured by the PSIM meta-ontology.

•

The navigator is a piece of software which enacts the links drawn in continuous line between tool users and centralized translator, application tools and centralized translator and within the centralized translation interface between the different components.


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Tool users PSIM Meta-Ontology

Centralized Tanslator

PSIM-procedure m odel

PSIM-Ontology

enterprise m odel

Com m unication layer

tool 1

tool 2

ERGO-tool

STSD-tool

tool 3 ERPsystem

tool n

Figure 2: General architecture of PSIM environment

3.2

PSIM ontology

As shown in the Figure 2, the main component of the centralized translator is the PSIM ontology component. It refers to a conceptual information model that specifies, explicitly and formally, a shared understanding of the enterprise. Within the PSIM environment it acts as a standardized reference model to facilitate interoperation and communication between applications with partly common terminology, and promotes the sharing of knowledge between the application tools [Uschold 1996] [Chandrasekaran B., Josephson J. R., Benjamins V. R. 1999]. Both the PSIM ontology and the PSIM-procedure model are specializations of a meta-ontology (Figure 2). The Meta-ontology focuses on the representation of the essential enterprise features, it consists of a model of three interrelated elements: activity, enterprise object, and information element. The relationships between these three elements are of two types: relevance linking any of the three elements to the information element, and underlining the fact that the information is relevant for the other element, and involved linking activity and enterprise object [PSIM Deliverable 2.2, 2001,pp. 42-47]. The PSIM ontology corresponds to a specialisation of the meta-ontology. This specialisation of the generic framework introduces other key concepts traditionally used in enterprise modelling [Vernadat, 1994], concepts such as routing activity, regular activity, resource (Figure 3).


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Figure 3: The PSIM-Ontology Specialisation of the PSIM Meta-Ontology

According to the ENV 40003 genericity dimension [ENV 40003, 1990], the meta-ontology belongs to the generic layer, the PSIM ontology to the partial layer and both the enterprise model and PSIM procedure model to the particular layer.

3.3

Example of Use: Translation layer with ergonomic concepts

The simplest case we have encountered is when there exists a one-to-one relationship between terms used in different languages. That means that for a term in a language A, there is a single corresponding term in the common language, which is translated in a single term in the language B. In this case the concepts are shared 1:1. The sequence of translation is represented in the Figure 4.

Figure 4: Simple translation

This kind of translation is the most currently met. In the following table, examples of terms from the ergonomic paradigm are presented, together with their translation in the common language. Task

Regular activity Description

Workstation

Procedure the regular activity has.

Organization element Description

Behavioural Model the organization element workstation has.

Task included

The set of activities, which are controlled by the organization element workstation.

Table 1: Example translation

Not all concepts do 1:1 correspond to shared concepts. Depending on the concepts, other kinds of translation are possible in the ontology we defined. For example, the difference of viewpoints


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can lead to a difference of granularity in the concepts manipulated in the analysis. This produces a 1:n relationship. We were confronted with this problem when we tried to translate the term action used in ergonomic analysis into a corresponding term in an ERP system and support the exchange of information concerning the associate concept. The ergonomic term action corresponds to the notion step in the PSIM ontology. This notion step exists in the ERP system, but does not exist independently of the concept activity: it is used in sequence to describe the procedure of an activity. We enable the exchange of data concerning action by adding in the PSIM ontology explicitly the relationship existing between the ergonomic concept action and the ERP concept step stored in the activity procedure. This relationship is the relationship refer to joining activity procedure and step. The support of the exchange of data in this case follows the general schema Figure 5.

Figure 5: Complex translation

If we apply it to the case of the action ergonomic terms, we obtain:

Figure 6: Example of complex translation

4

Conclusion and Future Work

PSIM recognizes that the new IT realities have a significant impact on knowledge management within organisations. PSIM has adopted an ontology based approach to the capturing and exchange of explicit enterprise knowledge, among people and software tools. Enterprise reference architectures and enterprise life-cycle concepts have been applied to systematize ontological commitment such that it will become possible to cope with challenges common to the enterprise domain: the growing importance and increasing frequency of changes to the enterprise operations; the co-existence of several relevant knowledge domains; the enormous diversity of particular enterprise domains; and the growing range of Information and Communication technologies used. These challenges exist even more for the network enterprise. Because the enterprise concepts on which PSIM has built, scale up to the network enterprise, it is anticipated that the enterprise ontology based techniques can also be extended to the network or extended enterprise: an important area for future work. Acknowledgement This work has been partly funded by the European Commission through IMS Project PSIM - Participative Simulation environment for Integral Manufacturing enterprise renewal (No. IMS 1999-00004). The authors wish to acknowledge the Commission for their support. We also wish to acknowledge the contribution of HUMACS and PSIM project partners to various ideas and concepts presented in this paper. References Berg, R.J. van den, Eijnatten, F.M. van, Vink, P., & Goossenaerts, J.B.M.: Leveraging human capital in assembly organizations: The case for participative simulation. Proceedings IST conference. Helsinki, 1999. Chandrasekaran B., Josephson J. R., Benjamins V. R.: What are ontologies, and why do we need them?. IEEE Intelligent System & their applications, Computer Society, January/February, 1999, pp. 20-26. Drucker, P.: ‘The new productivity challenge’, p.69-79, Nov-Dec, 1991


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