- Email: [email protected]
Knowledge Product Modelling for Industry: The PROMOTE Approach
Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012
Knowledge Product Modelling for Industry: The PROMOTE Approach Robert Woitsch*, Vedran Hrgovcic*, Robert Buchmann** * BOC Asset Management GmbH, Baeckerstrasse 5 1010 Vienna, Austria; +43 (01) 5120534; e-mail:{robert.woitsch/vedran.hrgovcic}@boc-eu.com ** Research Group Knowledge Engineering, University of Vienna Bruenner Strasse 72, 1210, Vienna, Austria e-mail: [email protected] Abstract: Modelling Knowledge is a necessity in case computer supported processing is applied. Hence the different level of formalisms for knowledge models and the resulting different application areas are discussed since the early stages of knowledge engineering and knowledge management. The cost and benefit calculation of the effort in modelling and the return on investment when applying processing on the models is still an unsolved challenge. The modelling element “Knowledge Product” is introduced by the modelling language PROMOTE® that enables a pragmatic, concrete and quick modelling of knowledge in enterprises. After this step, different KM scenarios for knowledge scorecards, processorientation, skill management or knowledge-based infrastructure can be realized. This paper introduces the overall model-based approach and focuses on the Knowledge Product element before introducing some lessons learned from research, teaching and commercial projects. Keywords: Knowledge Management, Modelling, Knowledge Space
1. INTRODUCTION When analyzing the transformation of the information society an industrialization of knowledge work can be observed. The maturity, the quality, the process-orientation and the alignment of knowledge to personal or organizational requirements are industrialization aspects covered by knowledge work. This paper focuses on model-oriented knowledge processing as a challenge that needs to be tackled not only on social and technical level but also on a conceptual level. Most prominently the Knowledge Product is discussed as it enables different KM-scenarios and is a modelling element that is not widely applied in the KM-community.
Internet of Things and Internet of Services as well as technology such as service-orientation, virtualization and mash-ups enable to develop orchestrations like knowledge conveyer belts, semantic workflows or knowledge buses. These technologies are introduced as a realization framework of model-oriented knowledge processing. This approach may be an answer for the requirements of industrialization of knowledge work that keeps the “human in the loop” and enables the alignment of business and knowledge. For the analysis of knowledge work, we follow the knowledge space approach of (Karagiannis, 2010). The knowledge space defines four dimensions: (1) Form, (2) Content, (3) Interpretation and (4) Use, which is introduced in Fig. 1.
As an expression of the maturity the knowledge work can be modelled in form of knowledge motivation, routines, situations, structure and vocabularies, elements and tools (compare (MATURE, 2009)). These models can either be expressed in a formal way in order to be preferably interpreted by machines in the context of Knowledge Engineering, or can be expressed in a semiformal – often graphical – way to be preferable interpreted by humans in the context of Knowledge Management. We experience a new level of knowledge maturity and industrialization of knowledge work and its model representation in both fields of Knowledge Engineering and Knowledge Management. Technological phenomena such as 978-3-902661-98-2/12/$20.00 © 2012 IFAC
Fig.1. The four dimensions of the Knowledge Space The form represents the syntax and semantic, like a group of human experts, text documents, models, program code,
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mathematical forms or statistics. The content is seen as the domain, in which knowledge techniques are applied. Hence all implicit or explicit expression of knowledge that is relevant is captured as content of the knowledge space. The use defines how knowledge techniques are applied like the communication between different domain experts, the assistance to express implicit knowledge in a formalized way, the comparison or assessment of content and the like. The representation of knowledge is either focused on machine interpretation – in terms of KE or on human interpretation - in terms of KM. KE focuses on machine interpretation, applying formalisms like ontologies, rules, workflows or agents, whereas KM focuses on human interpretation applying formalism like mind maps, guidelines, processes or social web. This paper presents some observations in the field of modeloriented knowledge processing, discusses the conceptualization of the concrete modelling language PROMOTE® and introduces the modelling element Knowledge Product, its application and lessons learned. Application scenarios of the described approach in the area of Virtual Organizations as well as managing of the Innovation in the production process, which are currently in the research focus of the authors, are also briefly introduced. 2 THE MODEL-BASED APPROACH FOR KNOWLEDGE In this section we will present identified model-based approaches for knowledge processing that have different levels of formalisms. In this section, first an overview on the different types of knowledge models is provided and second an approach to combine the different aspects of knowledge modelling is discussed. 2.1 Related Work
FIT (Karagiannis, 2008), ONTORULES (OntoRules, 2012), where the graphical representations of rules support the knowledge externalization. Goal modelling for agent based knowledge processing can be identified in BREIN (BREIN, 2012), (Schubert, et. al, 2008). Major focus in this area is the expression of knowledge in form of ontologies, or terminologies, like in OPAL (D’Antonio, et. al, 2007) or OWL (OWL, 2012). The creation, integration, harvesting, validation or evolution of ontologies is a hot research topics – cmp. SEKT (SEKT, 2012), SUPER (SUPER, 2012), INSEMTIVES (INSEMTIVES, 2012), and KIWI (KIWI, 2012). Hence KE can be applied using a model-based approach. Model Based Approaches for Knowledge Management Several initiatives contributed to the process-orientation in the context of knowledge (Hinkelmann, et. al, 2002). A list of approaches includes (Gronau, 2003): (1) the Income approach which links knowledge resources to processes, (2) the Workware approach which distinguishes between tacit knowledge and explicit knowledge, (3) the EULE2 is an agent based supporting system considering knowledge flows as processes; (4) the K-Modeller is a modelling method for knowledge-intensive business processes, (5) the ARIS extension provided additional modelling elements for EPC and (6) the DÉCOR (Abecker, 2001) approach - an EUProject investigating the link of ontologies with business processes. Today beside PROMOTE® (Karagiannis, 2000) which evolved from a series of research initiative towards a commercial product, there is also the KMDL (Gronau, 2005) approach that has reached the commercial level for knowledge management. A reference framework for processoriented knowledge management is provided from EuReKI (Heisig, 2007). Service Based Realization of Knowledge Management.
Model Based Approaches for Knowledge Engineering Models enable the externalization of knowledge in a machine interpretable form. As KE has its roots in artificial intelligence the same classification in symbolic, sub-symbolic and fuzzy logic can be used. It can be expressed in symbols represented in form of rules, frames, logic, predicate logic or concept maps to express static and dynamic knowledge. Often such formal and strict representations are difficult to define, when extracting knowledge out of the domain expert’s mind. Hence fuzzy logic has been introduced enabling a transformation from natural text into fuzzy logic. Knowledge that can not be expressed in symbols requires sub-symbolic techniques such as neural networks, which are an imitation of the human brain. We focus on the prominent Semantic Web approach, which has the vision of programs that can support tasks by intelligent mechanisms that were previously thought as of being solvable only by humans (W3C, 2012), (WSMO, 2012), (METEOR-S, 2012). In particular rule modelling has been identified in the project
The Service Oriented Knowledge Management (SOKM) is based on the assumption that successful implementation and execution of KM relies on tools, resources and humans that can be virtualized. Thus virtualization provides functionality as a service. The SOKM approach introduces the usage of Knowledge Services (K-S) on a conceptual level and on a technical level (Valente, 2011), (Woitsch, 2004). The service concept is therefore used for both the technical integration of different tools as well as the conceptual integration that considers the meaning of a service (FOI, 2012), This enables knowledge technology to participate in the trend towards the Internet of Services (Di Nitto, et. Al, 2009), (MATURE, 2009) by providing encapsulated knowledge tools via services. The key challenge is the definition of the “meaning” of knowledge services. This is approached by defining formal models for services or practical codes (MATURE, 2009). Knowledge-Domain Modelling Often the domain knowledge is already available in form of a model. We identified domain-specific models as a form of
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externalized knowledge like in business process models (e.g. using BPMN), in IT-management models (e.g. using ITIL), in enterprise modelling using the Zachmann framework or in the logistics domain using SCOR Method. Hence additionally to the focus of knowledge models, the Open Knowledge Models (OKM) needs a concept that allows the integration of domain models in – potentially – any format to be included in the OKM knowledge representation. 2.2 Knowledge Modelling and Meta Modelling As discussed in the previous section knowledge models are considered as an instrument to formally externalize knowledge as they are a “representation of either reality or vision” (Strahringer, 1996) representing the real world in an agreed syntax and semantics. The goal of OKM is to develop a conceptual integration between formalized knowledge models – mainly in the domain of knowledge engineering – and semi-formal or informal knowledge models – mainly in the domain of knowledge management – to integrate both knowledge modelling aspects and to consider the semantic loss. As a solution the meta model approach is selected, as both knowledge engineering and knowledge management models can be formulated in meta-models. The meta model approach depicting the layered model stack by (Strahringer, 1996) and adapted by (Karagiannis, 2006) defines modelling languages by syntax, semantics and notation in order to provide the necessary modelling primitives for building models. The concepts that describe the modelling language are defined in the meta meta model language. A prominent meta modelling framework is the ADOxx® meta meta model, which has been developed at the University of Vienna, and implemented in the commercial tool ADONIS®. Another prominent framework is MOF – Meta Object Facilities (OMG, 2012) – from which the ontology language OWL and the rule language SWRL can be deduced. Knowledge models and knowledge modelling languages can be defined using one of the aforementioned meta-models in order to transform, exchange, reference and integrate models (Kühn, 2004), (Kühn et. Al, 2003). The ADOxx® meta model has been selected and will be focused in the rest of the paper. The initial implementation of a technical prototype has been performed in the EU-project plugIT. The mechanism for combining formal knowledge models, in case of plugIT OWL and SWRL with semi-formal knowledge models, in case of plugIT PROMOTE® has been implemented in a so-called Semantic Modelling Kernel.
(a) Knowledge-based Product Strategy: This KM-scenario supports the high level monitoring of information and knowledge management applying a so-called knowledge scorecard. Reference projects have been published with the Austrian Military on knowledge balances (Göllner, et. al, 2008). (b) Knowledge-based Processes: This KM-scenario supports process-oriented knowledge management by interpreting business processes not only as logical sequence of tasks but as the know-how platform of an enterprise. Reference projects have been published in the domain of software development (Woitsch, 2009). (c) Knowledge-based Organisation: This KM-scenario supports skill- and trainings management by deriving required competences and skills from provided products. Reference projects have been published in cooperation with the Austrian Military on skill management and team assessment (Göllner, et. al, 2008). (d) Knowledge-based Infrastructure: This KM-scenario supports the usage of the infrastructure by introducing knowledge management processes that are knowledge flows between knowledge workers. Reference project has been published in the home textile industry (Woitsch et. al, 2008). 3.2 Conceptualisation of the Knowledge Modelling Method The key challenge is, to identify a generic framework that enables the particularization of each knowledge model. The modelling method framework is seen as such generic framework that enables the particularization of all existing knowledge models and it is argued that knowledge models that are not yet developed can also be particularized with this framework.
3 THE PROMOTE MODELLING LANGUAGE The OPENMODELS Initiative (OMI, 2012) proposes an open platform for models and modelling languages in a similar way like open source does for software. In the following the conceptualization of the PROMOTE modelling language is discussed. 3.1 Application Scenarios of Knowledge Modelling Method
Fig. 2. Modelling Method Framework, source (Karagiannis 2002) Fig. 2 introduces the key parts that define a modelling method (see (Karagiannis 2002), (Murzek, 2008), (plugIT, 2012) for details). See (Karagiannis 2002) for a high resolution graphic.
PROMOTE® distinguishes four knowledge management scenarios.
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(1) The modelling language The PROMOTE modelling language is explained in (PROMOTE, 2012). In this paper we would like to introduce and focus on the most important anchor element for knowledge modelling in enterprises. It is the concept of knowledge product that defines knowledge in a consumable way. The mechanisms of social economics and the principles of non-physical products are applied for knowledge as the value of knowledge is defined by the consumer. Hence applying the principles of products according to (Kotler, 2001) for knowledge enables explicit definition of knowledge demand, knowledge provisioning and knowledge monitoring and assessment. The collection of all relevant knowledge products is the starting point for all application scenarios of knowledge modelling. It is package in the model type “knowledge produce model” that includes the model element “knowledge product”. Knowledge product is defined by the following properties P: PName : A text string that defines a human readable name. PID : A unique identifier. PDescription: A text field that explains the knowledge product for persons those are usually not familiar or working with this product. PComment: A text file that explains some insight aspects of the knowledge product for persons those are usually familiar with the product and work with it regularly. PDokumentation_of_Product: A file in form of regulations, guidelines of handbook that explains in detail the usage of the knowledge product. PProduct_type: A selection of either (a) Information product, which supports the Explicitation of knowledge, (b) Information service, which support the socialisation of knowledge or (c) Application, which supports the combination of knowledge. Hence all forms or knowledge provision is classified, as implicitation is not a provision. PProduct_category: A text field that enables a self-classification e.g. based on departments. PActuality: A selection of either (a) daily, (b) weekly, (c) monthly, (d) periodically, (e) yearly, (f) on demand that describes the interval the knowledge product is created or updated. PResponsible_Persion: The definition of the person that is responsible for creating the product. PProducing_Process: The definition of the process how this product is created. PAccess_Requirements: A text field that enables the description under which conditions this product can be consumed. PLocation: A text field that describes the physical location of the product.
PReferenced_Skills: A list of references to skills. In PROMOTE® skills are defined in form of topics; hence a list of topics defines the required skills. PReferenced_Sources: In case the knowledge product can be accessed electronically, this property enables the link to access the knowledge product. Hence a knowledge product model consists of a collection of knowledge product elements which is typically presented in a hierarchical structure. (2) The modelling procedure PROMOTE® proposes the knowledge product models as starting points for all knowledge management scenarios. Hence there are different modelling procedures depending on enterprise environment. Process Oriented modelling procedure starts with relevant business process. Each phase is analysed according the used knowledge products. The list of knowledge products is structured according the phases in hierarchical order. Organisational Oriented modelling procedure starts with relevant organisational units and departments. Each department lists it’s consumption of knowledge products and it’s creation of knowledge products. Technical Oriented modelling procedure starts with an existing platform such as a Web-Portal or a collection of technical solutions and analysis each product that is provided on the technical solution. Starting form this, also nontechnical knowledge products are identified. Document Oriented modelling procedure starts with guidelines, rules or handbook documents. The documents are read and analysed, which knowledge product is required or created. In praxis different approaches are combined or used vice versa as quality approval of the knowledge produce model. (3) The mechanism and algorithms Beside the basis mechanisms such as modelling, analysis and documentation in form of model reports there are two functions implemented for knowledge products. f Creation_of_product_brief : this function creates a fact sheet for each knowledge product in Text format. These fact sheets are typically 1-2 A4 pages per product in WORD format. f Creation_of_Skill_Lists : this function creates required skill lists for each knowledge product in Spread Sheet format. These skills are typically on the level a working place is specified and vary depending on the level of detail and the environment of the product. 3.3 Application of the Knowledge Modelling Method This section introduces samples on knowledge product models for the aforementioned four KM-scenarios supported by PROMOTE®.
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4 LESSONS LEARNED OF KNOWLEDGE MODELLING METHODS AND FUTURE WORK Knowledge Product models have been identified as the starting point for all KM-scenarios as it provide knowledge in a consumable and hence identifiable and assessable format. It can be observed that the aforementioned modelling procedures always result in a complete knowledge product.
Fig. 3. A Sample Knowledge Product Model Fig. 3. introduces a sample knowledge product model from an organisational unit that is divided in four segments. One segment deals with knowledge products such as guidelines that steer the other three organisational units; hence those five products are the indicated five red dots on top of the model. Please see (PROMOTE, 2012) for high resolution images. Three departments list their products in hierarchical order; hence they are grouped and listed in parallel. (a) Knowledge-based Product strategy using the knowledge balances started by first defining the knowledge products. A reference project of the Austrian Military can be referenced. Following a Document-Oriented modelling approach resulted in 16 different sub-knowledge product models and in about 150 knowledge products in total. Knowledge products have been categorised on organisational level, on its level of maturity (early prototypes, guidelines …) and result types. Based on the knowledge product model, the key performance indicators have been identified on the user feedback per product, the effort to create knowledge products, estimations on the usability of the knowledge products, etc. (b) Knowledge-based Process strategy was applied in a software development project. According the software development process the knowledge products have been identified. In total twenty products such as Web-pages, Wiki, Yellow Pages or Java-Doc have been identified and aligned with the overall software development process. This ensured the coordinated usage and provision of the various platforms that have been interpreted as knowledge products. (c) Knowledge-based Organisation was applied in a reorganisation project, where an organisational unit has been transformed. Based on 93 knowledge products following a Document-based approach there where 850 skill elements that where required. Manual simulations have been applied using Excel to assess different variations of re-organisations and the effect on the provision of the required knowledge. (d) Knowledge-based Infrastructure was applied in research project for the home textile domain. Two knowledge products have been identified and compared: a Web-Shop without smart components and a Web-shop including smart components. The underlying knowledge management processes where different, hence the knowledge products where the starting point for identifying the underlying processes.
It can also be observed that the quality and the level of detail and hence usability of the knowledge product model, strongly depend on the participating domain experts but equally important on the defined goals of the KM-projects. KM-initiatives with clear and concrete goals enable a concrete and detailed clarification of relevant knowledge products. In a series of research, teaching and commercial projects, the identification of knowledge products was always the starting point that lead in a minimum of time (usually 2-3 workshops) to concrete, manageable and applicable results. Due to dynamic and rapid changes in the business and production environment nowadays, additional factors became important when applying the knowledge product modelling technique. On one hand the ability to exchange production partners “on-the-fly” when producing in the so-called Virtual Enterprises environments imposed the requirement that Knowledge Product can be (1) created collaboratively, (2) applied over enterprise-boundaries and (3) be accessible according to the role or skill based dynamic access control model. On the other hand, the innovation process, one of the key-factors in almost any industry was also affected, thus requesting for a novel ways of approaching the innovation based on knowledge product in such way that crossing the Virtual Enterprise boundaries is feasible without hampering the innovation process. The first research challenge is currently tackled in the ComVantage research project (ComVantage, 2012) where one of the key research points in creating a “Collaborative Manufacturing Network for Competitive Advantage” is the goal to create a methodology of enabling secure and decentralised access to the Knowledge Products available in the Virtual Enterprise. The second challenge, as already described is directed toward developing a conceptual as well as technical solution (socalled Mission Control Room for managing innovation processes in the virtual production space of a Virtual Enterprise) to changes imposed over an innovation process in a highly dynamic Virtual Enterprise Domain is being investigated in the BIVEE (BIVEE, 2012) project. REFERENCES Abecker, A., Mentzas, G., Legal, M., Ntioudis, S. and Papavassiliou, G. (2001): Business Process Oriented Delivery of Knowledge through Domain Ontologies, Proceedings of DEXA conference, Munich,
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BREIN EU-project (2012), Homepage: http://www.eubrein.com, 07.03.2012 BIVEE (2012), EU Project, http://www.bivee.eu, Access: 07.03.2012 ComVantage (2012), EU-Project, http://www.comvantage.eu, Access: 07.03.2012 D'Antonio. F., Missikoff. M., Taglino, F. (2007): Formalizing the OPAL eBusiness ontology design patterns with OWL FOI (2012) :Future of Internet, http://www.fi-bled.eu/, Access: 07.03.2012 Göllner J., Mak K., Trattnig G., Woitsch R. (2008): Wissensmanagement und Wissensbilanz im ÖBH am Beispiel ABCAbwS & ABCABw, Sonderpublication ISBN: 978-3-902670-00-7, Vienna Gronau N. (2003): Modellieren von wissensintensiven Geschäftsprozesses mit der Beschreibungssprache KModeler, in Gronau N. Wissensmanagement: Potentiale – Konzepte - Werkzeuge, GITO Verlag, p3-30 Gronau, N. Bahrs, J, (2005): Prozessorientiertes Wissensmangement – Strategien, Methoden, Erfahrungen und Werkzeuge -, GITO Verlag, Berlin Heisig, P., Orth, R. (2007): Knowledge Management Framework. An international comparative Study Berlin Cambridge: Eureki 2007 ISBN 978-3-00-020575-0 Hinkelmann, K., Karagiannis, D., Telesko, R. (2002): PROMOTE - Methodologie und Werkzeug zum geschäftsprozessorientierten Wissensmanagement. In: Geschäftsprozessorientiertes Wissensmanagement, Springer-Verlag INSEMTIVES EU-Project (2012), http://www.insemtives.org/, Access: 07.03.2012 Karagiannis, D. and Telesko, R. (2000): The EU-Project PROMOTE: A Process-oriented Approach for Knowledge Management, Proceedings of the 3rd International Conference on Practical Aspects of Knowledge Management, Basel Karagiannis, D.; Kühn, H. (2002): Metamodelling Platforms. Invited Paper. In: Bauknecht, K.; Min Tjoa, A.; Quirchmayer, G. (Eds.): EC-Web– Dexa 2002, France, LNCS 2455, Springer-Verlag, Berlin, p. 182. Karagiannis, D., Höfferer, P. (2006): Metamodels in Action: An overview, In: J. Filipe & B. Shishkov & M. Helfert (Eds.), First International Conference on Software and Data Technologies:IS27-36. Setúbal: Insticc Press Karagiannis, D.; Utz, W., Woitsch, R. and Leutgeb, A. (2008): Business Processes and Rules: An eGovernment Case-Study. In: Hinkelmann, K. and Thönssen, B.: AAAI Spring Symposium AI Meets Business Rules and Process Management. Stanford, University, California Karagiannis D., Woitsch R. (2010), Knowledge Engineering in Business Process Management, In vom Brocke J., Rosemann M., (eds.) Handboork on Business Process Management 2, International Handbooks on Information Systems. Springer-Verlag Berlin Heidelberg KIWI EU-Project (2012), http://www.kiwi-project.eu/, Access: 07.03.2012 Kotler, P., Bliemel, F., (2001) Marketing-Management, 10. Aufl., Schäffer-Poeschel, Stuttgart, S. 102 Kühn, H., Bayer, F., Junginger, S., Karagiannis, D. (2003): enterprise model integration. In proceedings of the 4th
international conference Dexa 2003, Czech Republic, lncs 2738, Springer-Verlag, Berlin, pp. 379-392. Kühn, H., (2004): PhD Thesis: Methodenintegration im Business Engineering, University of Vienna MATURE D1.1 (2009): Results of the Ethnographic Study and Conceptual Knowledge Maturing Model METEOR-S (2012), Semantic Web Services and Processes, http://lsdis.cs.uga.edu/projects/meteors/index.php?page=1, access: 07.03.2012 Murzek, M. (2008). The Model Morphing Approach Horizontal Transformation of Business Process Models, PhD Thesis OMG (2012), Object Management Group, Meta Object Facility (MOF), http://www.omg.org/mof/, Access: 07.03.2012 OMI (2012) Open Models Initiative, www.openmodels.at, Access: 07.03.2012 OntoRules EU-Project (2012), http://ontorule-project.eu/, Access: 07.03.2012 OWL (2012), http://www.w3.org/TR/owl2-overview/, Access: 07.03.2012 plugIT (2012) EU-project, www.plug-it.org, Access: 07.03.2012 PROMOTE (2012), Modelling Language Specification, http://83.65.190.85/promote_repository/papers/PROMO TE%20Modelling%20Language%20Specification.pdf, Access: 07.03.2012 Schubert. L., Kipp, A. Wesner, S. (2008): "Supporting Collaborative Engineering Using an Intelligent Web Service Middleware", Journal of Advanced Engineering Informatics, vol. 22, num. 4 SEKT EU-Project (2012), http://www.sekt-project.com/, Access: 07.03.2012 Strahringer, S. (1996): Metamodellierung als Instrument des Methodenvergleichs: eine Evaluierung am Beispiel objektorientierter Analysemethoden. Shaker, Aachen, SUPER EU-Project (2012), http://www.ip-super.org/, Access: 07.03.2012 Valente, A and Housel, T. (2001): A Framework to Analyze and Compare Knowledge Management Tools, In Proceedings of the Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, IOS Press, Ohmsha Woitsch, R. (2004): PhD Thesis, Process Oriented Knowledge Management: A Service-Based Approach, PhD thesis University of Vienna Woitsch R., Karagiannis D., Fill H.-G. Blazevic V. (2008): Semantic Based Knowledge Flow System in Euopean Home Textile: A Process Oriented Approach with PROMOTE, I-Know, Graz Woitsch R., Leutgeb A. (2009): The BREIN-Roadmap with PROMOTE: A Use-Case Scenario of a Service-Based Knowledge Management Approach, I-Know, Graz WSMO, Web Service Modeling Ontology (2012), http://www.wsmo.org/, Access: 07.03.2012 W3C, OWL-S (2012): Semantic Markup for Web Services, http://www.w3.org/Submission/OWL-S/, Access: 07.03.2012
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Knowledge Product Modelling for Industry: The PROMOTE Approach Robert Woitsch*, Vedran Hrgovcic*, Robert Buchmann** * BOC Asset Management GmbH, Baeckerstrasse 5 1010 Vienna, Austria; +43 (01) 5120534; e-mail:{robert.woitsch/vedran.hrgovcic}@boc-eu.com ** Research Group Knowledge Engineering, University of Vienna Bruenner Strasse 72, 1210, Vienna, Austria e-mail: [email protected] Abstract: Modelling Knowledge is a necessity in case computer supported processing is applied. Hence the different level of formalisms for knowledge models and the resulting different application areas are discussed since the early stages of knowledge engineering and knowledge management. The cost and benefit calculation of the effort in modelling and the return on investment when applying processing on the models is still an unsolved challenge. The modelling element “Knowledge Product” is introduced by the modelling language PROMOTE® that enables a pragmatic, concrete and quick modelling of knowledge in enterprises. After this step, different KM scenarios for knowledge scorecards, processorientation, skill management or knowledge-based infrastructure can be realized. This paper introduces the overall model-based approach and focuses on the Knowledge Product element before introducing some lessons learned from research, teaching and commercial projects. Keywords: Knowledge Management, Modelling, Knowledge Space
1. INTRODUCTION When analyzing the transformation of the information society an industrialization of knowledge work can be observed. The maturity, the quality, the process-orientation and the alignment of knowledge to personal or organizational requirements are industrialization aspects covered by knowledge work. This paper focuses on model-oriented knowledge processing as a challenge that needs to be tackled not only on social and technical level but also on a conceptual level. Most prominently the Knowledge Product is discussed as it enables different KM-scenarios and is a modelling element that is not widely applied in the KM-community.
Internet of Things and Internet of Services as well as technology such as service-orientation, virtualization and mash-ups enable to develop orchestrations like knowledge conveyer belts, semantic workflows or knowledge buses. These technologies are introduced as a realization framework of model-oriented knowledge processing. This approach may be an answer for the requirements of industrialization of knowledge work that keeps the “human in the loop” and enables the alignment of business and knowledge. For the analysis of knowledge work, we follow the knowledge space approach of (Karagiannis, 2010). The knowledge space defines four dimensions: (1) Form, (2) Content, (3) Interpretation and (4) Use, which is introduced in Fig. 1.
As an expression of the maturity the knowledge work can be modelled in form of knowledge motivation, routines, situations, structure and vocabularies, elements and tools (compare (MATURE, 2009)). These models can either be expressed in a formal way in order to be preferably interpreted by machines in the context of Knowledge Engineering, or can be expressed in a semiformal – often graphical – way to be preferable interpreted by humans in the context of Knowledge Management. We experience a new level of knowledge maturity and industrialization of knowledge work and its model representation in both fields of Knowledge Engineering and Knowledge Management. Technological phenomena such as 978-3-902661-98-2/12/$20.00 © 2012 IFAC
Fig.1. The four dimensions of the Knowledge Space The form represents the syntax and semantic, like a group of human experts, text documents, models, program code,
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mathematical forms or statistics. The content is seen as the domain, in which knowledge techniques are applied. Hence all implicit or explicit expression of knowledge that is relevant is captured as content of the knowledge space. The use defines how knowledge techniques are applied like the communication between different domain experts, the assistance to express implicit knowledge in a formalized way, the comparison or assessment of content and the like. The representation of knowledge is either focused on machine interpretation – in terms of KE or on human interpretation - in terms of KM. KE focuses on machine interpretation, applying formalisms like ontologies, rules, workflows or agents, whereas KM focuses on human interpretation applying formalism like mind maps, guidelines, processes or social web. This paper presents some observations in the field of modeloriented knowledge processing, discusses the conceptualization of the concrete modelling language PROMOTE® and introduces the modelling element Knowledge Product, its application and lessons learned. Application scenarios of the described approach in the area of Virtual Organizations as well as managing of the Innovation in the production process, which are currently in the research focus of the authors, are also briefly introduced. 2 THE MODEL-BASED APPROACH FOR KNOWLEDGE In this section we will present identified model-based approaches for knowledge processing that have different levels of formalisms. In this section, first an overview on the different types of knowledge models is provided and second an approach to combine the different aspects of knowledge modelling is discussed. 2.1 Related Work
FIT (Karagiannis, 2008), ONTORULES (OntoRules, 2012), where the graphical representations of rules support the knowledge externalization. Goal modelling for agent based knowledge processing can be identified in BREIN (BREIN, 2012), (Schubert, et. al, 2008). Major focus in this area is the expression of knowledge in form of ontologies, or terminologies, like in OPAL (D’Antonio, et. al, 2007) or OWL (OWL, 2012). The creation, integration, harvesting, validation or evolution of ontologies is a hot research topics – cmp. SEKT (SEKT, 2012), SUPER (SUPER, 2012), INSEMTIVES (INSEMTIVES, 2012), and KIWI (KIWI, 2012). Hence KE can be applied using a model-based approach. Model Based Approaches for Knowledge Management Several initiatives contributed to the process-orientation in the context of knowledge (Hinkelmann, et. al, 2002). A list of approaches includes (Gronau, 2003): (1) the Income approach which links knowledge resources to processes, (2) the Workware approach which distinguishes between tacit knowledge and explicit knowledge, (3) the EULE2 is an agent based supporting system considering knowledge flows as processes; (4) the K-Modeller is a modelling method for knowledge-intensive business processes, (5) the ARIS extension provided additional modelling elements for EPC and (6) the DÉCOR (Abecker, 2001) approach - an EUProject investigating the link of ontologies with business processes. Today beside PROMOTE® (Karagiannis, 2000) which evolved from a series of research initiative towards a commercial product, there is also the KMDL (Gronau, 2005) approach that has reached the commercial level for knowledge management. A reference framework for processoriented knowledge management is provided from EuReKI (Heisig, 2007). Service Based Realization of Knowledge Management.
Model Based Approaches for Knowledge Engineering Models enable the externalization of knowledge in a machine interpretable form. As KE has its roots in artificial intelligence the same classification in symbolic, sub-symbolic and fuzzy logic can be used. It can be expressed in symbols represented in form of rules, frames, logic, predicate logic or concept maps to express static and dynamic knowledge. Often such formal and strict representations are difficult to define, when extracting knowledge out of the domain expert’s mind. Hence fuzzy logic has been introduced enabling a transformation from natural text into fuzzy logic. Knowledge that can not be expressed in symbols requires sub-symbolic techniques such as neural networks, which are an imitation of the human brain. We focus on the prominent Semantic Web approach, which has the vision of programs that can support tasks by intelligent mechanisms that were previously thought as of being solvable only by humans (W3C, 2012), (WSMO, 2012), (METEOR-S, 2012). In particular rule modelling has been identified in the project
The Service Oriented Knowledge Management (SOKM) is based on the assumption that successful implementation and execution of KM relies on tools, resources and humans that can be virtualized. Thus virtualization provides functionality as a service. The SOKM approach introduces the usage of Knowledge Services (K-S) on a conceptual level and on a technical level (Valente, 2011), (Woitsch, 2004). The service concept is therefore used for both the technical integration of different tools as well as the conceptual integration that considers the meaning of a service (FOI, 2012), This enables knowledge technology to participate in the trend towards the Internet of Services (Di Nitto, et. Al, 2009), (MATURE, 2009) by providing encapsulated knowledge tools via services. The key challenge is the definition of the “meaning” of knowledge services. This is approached by defining formal models for services or practical codes (MATURE, 2009). Knowledge-Domain Modelling Often the domain knowledge is already available in form of a model. We identified domain-specific models as a form of
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externalized knowledge like in business process models (e.g. using BPMN), in IT-management models (e.g. using ITIL), in enterprise modelling using the Zachmann framework or in the logistics domain using SCOR Method. Hence additionally to the focus of knowledge models, the Open Knowledge Models (OKM) needs a concept that allows the integration of domain models in – potentially – any format to be included in the OKM knowledge representation. 2.2 Knowledge Modelling and Meta Modelling As discussed in the previous section knowledge models are considered as an instrument to formally externalize knowledge as they are a “representation of either reality or vision” (Strahringer, 1996) representing the real world in an agreed syntax and semantics. The goal of OKM is to develop a conceptual integration between formalized knowledge models – mainly in the domain of knowledge engineering – and semi-formal or informal knowledge models – mainly in the domain of knowledge management – to integrate both knowledge modelling aspects and to consider the semantic loss. As a solution the meta model approach is selected, as both knowledge engineering and knowledge management models can be formulated in meta-models. The meta model approach depicting the layered model stack by (Strahringer, 1996) and adapted by (Karagiannis, 2006) defines modelling languages by syntax, semantics and notation in order to provide the necessary modelling primitives for building models. The concepts that describe the modelling language are defined in the meta meta model language. A prominent meta modelling framework is the ADOxx® meta meta model, which has been developed at the University of Vienna, and implemented in the commercial tool ADONIS®. Another prominent framework is MOF – Meta Object Facilities (OMG, 2012) – from which the ontology language OWL and the rule language SWRL can be deduced. Knowledge models and knowledge modelling languages can be defined using one of the aforementioned meta-models in order to transform, exchange, reference and integrate models (Kühn, 2004), (Kühn et. Al, 2003). The ADOxx® meta model has been selected and will be focused in the rest of the paper. The initial implementation of a technical prototype has been performed in the EU-project plugIT. The mechanism for combining formal knowledge models, in case of plugIT OWL and SWRL with semi-formal knowledge models, in case of plugIT PROMOTE® has been implemented in a so-called Semantic Modelling Kernel.
(a) Knowledge-based Product Strategy: This KM-scenario supports the high level monitoring of information and knowledge management applying a so-called knowledge scorecard. Reference projects have been published with the Austrian Military on knowledge balances (Göllner, et. al, 2008). (b) Knowledge-based Processes: This KM-scenario supports process-oriented knowledge management by interpreting business processes not only as logical sequence of tasks but as the know-how platform of an enterprise. Reference projects have been published in the domain of software development (Woitsch, 2009). (c) Knowledge-based Organisation: This KM-scenario supports skill- and trainings management by deriving required competences and skills from provided products. Reference projects have been published in cooperation with the Austrian Military on skill management and team assessment (Göllner, et. al, 2008). (d) Knowledge-based Infrastructure: This KM-scenario supports the usage of the infrastructure by introducing knowledge management processes that are knowledge flows between knowledge workers. Reference project has been published in the home textile industry (Woitsch et. al, 2008). 3.2 Conceptualisation of the Knowledge Modelling Method The key challenge is, to identify a generic framework that enables the particularization of each knowledge model. The modelling method framework is seen as such generic framework that enables the particularization of all existing knowledge models and it is argued that knowledge models that are not yet developed can also be particularized with this framework.
3 THE PROMOTE MODELLING LANGUAGE The OPENMODELS Initiative (OMI, 2012) proposes an open platform for models and modelling languages in a similar way like open source does for software. In the following the conceptualization of the PROMOTE modelling language is discussed. 3.1 Application Scenarios of Knowledge Modelling Method
Fig. 2. Modelling Method Framework, source (Karagiannis 2002) Fig. 2 introduces the key parts that define a modelling method (see (Karagiannis 2002), (Murzek, 2008), (plugIT, 2012) for details). See (Karagiannis 2002) for a high resolution graphic.
PROMOTE® distinguishes four knowledge management scenarios.
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(1) The modelling language The PROMOTE modelling language is explained in (PROMOTE, 2012). In this paper we would like to introduce and focus on the most important anchor element for knowledge modelling in enterprises. It is the concept of knowledge product that defines knowledge in a consumable way. The mechanisms of social economics and the principles of non-physical products are applied for knowledge as the value of knowledge is defined by the consumer. Hence applying the principles of products according to (Kotler, 2001) for knowledge enables explicit definition of knowledge demand, knowledge provisioning and knowledge monitoring and assessment. The collection of all relevant knowledge products is the starting point for all application scenarios of knowledge modelling. It is package in the model type “knowledge produce model” that includes the model element “knowledge product”. Knowledge product is defined by the following properties P: PName : A text string that defines a human readable name. PID : A unique identifier. PDescription: A text field that explains the knowledge product for persons those are usually not familiar or working with this product. PComment: A text file that explains some insight aspects of the knowledge product for persons those are usually familiar with the product and work with it regularly. PDokumentation_of_Product: A file in form of regulations, guidelines of handbook that explains in detail the usage of the knowledge product. PProduct_type: A selection of either (a) Information product, which supports the Explicitation of knowledge, (b) Information service, which support the socialisation of knowledge or (c) Application, which supports the combination of knowledge. Hence all forms or knowledge provision is classified, as implicitation is not a provision. PProduct_category: A text field that enables a self-classification e.g. based on departments. PActuality: A selection of either (a) daily, (b) weekly, (c) monthly, (d) periodically, (e) yearly, (f) on demand that describes the interval the knowledge product is created or updated. PResponsible_Persion: The definition of the person that is responsible for creating the product. PProducing_Process: The definition of the process how this product is created. PAccess_Requirements: A text field that enables the description under which conditions this product can be consumed. PLocation: A text field that describes the physical location of the product.
PReferenced_Skills: A list of references to skills. In PROMOTE® skills are defined in form of topics; hence a list of topics defines the required skills. PReferenced_Sources: In case the knowledge product can be accessed electronically, this property enables the link to access the knowledge product. Hence a knowledge product model consists of a collection of knowledge product elements which is typically presented in a hierarchical structure. (2) The modelling procedure PROMOTE® proposes the knowledge product models as starting points for all knowledge management scenarios. Hence there are different modelling procedures depending on enterprise environment. Process Oriented modelling procedure starts with relevant business process. Each phase is analysed according the used knowledge products. The list of knowledge products is structured according the phases in hierarchical order. Organisational Oriented modelling procedure starts with relevant organisational units and departments. Each department lists it’s consumption of knowledge products and it’s creation of knowledge products. Technical Oriented modelling procedure starts with an existing platform such as a Web-Portal or a collection of technical solutions and analysis each product that is provided on the technical solution. Starting form this, also nontechnical knowledge products are identified. Document Oriented modelling procedure starts with guidelines, rules or handbook documents. The documents are read and analysed, which knowledge product is required or created. In praxis different approaches are combined or used vice versa as quality approval of the knowledge produce model. (3) The mechanism and algorithms Beside the basis mechanisms such as modelling, analysis and documentation in form of model reports there are two functions implemented for knowledge products. f Creation_of_product_brief : this function creates a fact sheet for each knowledge product in Text format. These fact sheets are typically 1-2 A4 pages per product in WORD format. f Creation_of_Skill_Lists : this function creates required skill lists for each knowledge product in Spread Sheet format. These skills are typically on the level a working place is specified and vary depending on the level of detail and the environment of the product. 3.3 Application of the Knowledge Modelling Method This section introduces samples on knowledge product models for the aforementioned four KM-scenarios supported by PROMOTE®.
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4 LESSONS LEARNED OF KNOWLEDGE MODELLING METHODS AND FUTURE WORK Knowledge Product models have been identified as the starting point for all KM-scenarios as it provide knowledge in a consumable and hence identifiable and assessable format. It can be observed that the aforementioned modelling procedures always result in a complete knowledge product.
Fig. 3. A Sample Knowledge Product Model Fig. 3. introduces a sample knowledge product model from an organisational unit that is divided in four segments. One segment deals with knowledge products such as guidelines that steer the other three organisational units; hence those five products are the indicated five red dots on top of the model. Please see (PROMOTE, 2012) for high resolution images. Three departments list their products in hierarchical order; hence they are grouped and listed in parallel. (a) Knowledge-based Product strategy using the knowledge balances started by first defining the knowledge products. A reference project of the Austrian Military can be referenced. Following a Document-Oriented modelling approach resulted in 16 different sub-knowledge product models and in about 150 knowledge products in total. Knowledge products have been categorised on organisational level, on its level of maturity (early prototypes, guidelines …) and result types. Based on the knowledge product model, the key performance indicators have been identified on the user feedback per product, the effort to create knowledge products, estimations on the usability of the knowledge products, etc. (b) Knowledge-based Process strategy was applied in a software development project. According the software development process the knowledge products have been identified. In total twenty products such as Web-pages, Wiki, Yellow Pages or Java-Doc have been identified and aligned with the overall software development process. This ensured the coordinated usage and provision of the various platforms that have been interpreted as knowledge products. (c) Knowledge-based Organisation was applied in a reorganisation project, where an organisational unit has been transformed. Based on 93 knowledge products following a Document-based approach there where 850 skill elements that where required. Manual simulations have been applied using Excel to assess different variations of re-organisations and the effect on the provision of the required knowledge. (d) Knowledge-based Infrastructure was applied in research project for the home textile domain. Two knowledge products have been identified and compared: a Web-Shop without smart components and a Web-shop including smart components. The underlying knowledge management processes where different, hence the knowledge products where the starting point for identifying the underlying processes.
It can also be observed that the quality and the level of detail and hence usability of the knowledge product model, strongly depend on the participating domain experts but equally important on the defined goals of the KM-projects. KM-initiatives with clear and concrete goals enable a concrete and detailed clarification of relevant knowledge products. In a series of research, teaching and commercial projects, the identification of knowledge products was always the starting point that lead in a minimum of time (usually 2-3 workshops) to concrete, manageable and applicable results. Due to dynamic and rapid changes in the business and production environment nowadays, additional factors became important when applying the knowledge product modelling technique. On one hand the ability to exchange production partners “on-the-fly” when producing in the so-called Virtual Enterprises environments imposed the requirement that Knowledge Product can be (1) created collaboratively, (2) applied over enterprise-boundaries and (3) be accessible according to the role or skill based dynamic access control model. On the other hand, the innovation process, one of the key-factors in almost any industry was also affected, thus requesting for a novel ways of approaching the innovation based on knowledge product in such way that crossing the Virtual Enterprise boundaries is feasible without hampering the innovation process. The first research challenge is currently tackled in the ComVantage research project (ComVantage, 2012) where one of the key research points in creating a “Collaborative Manufacturing Network for Competitive Advantage” is the goal to create a methodology of enabling secure and decentralised access to the Knowledge Products available in the Virtual Enterprise. The second challenge, as already described is directed toward developing a conceptual as well as technical solution (socalled Mission Control Room for managing innovation processes in the virtual production space of a Virtual Enterprise) to changes imposed over an innovation process in a highly dynamic Virtual Enterprise Domain is being investigated in the BIVEE (BIVEE, 2012) project. REFERENCES Abecker, A., Mentzas, G., Legal, M., Ntioudis, S. and Papavassiliou, G. (2001): Business Process Oriented Delivery of Knowledge through Domain Ontologies, Proceedings of DEXA conference, Munich,
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BREIN EU-project (2012), Homepage: http://www.eubrein.com, 07.03.2012 BIVEE (2012), EU Project, http://www.bivee.eu, Access: 07.03.2012 ComVantage (2012), EU-Project, http://www.comvantage.eu, Access: 07.03.2012 D'Antonio. F., Missikoff. M., Taglino, F. (2007): Formalizing the OPAL eBusiness ontology design patterns with OWL FOI (2012) :Future of Internet, http://www.fi-bled.eu/, Access: 07.03.2012 Göllner J., Mak K., Trattnig G., Woitsch R. (2008): Wissensmanagement und Wissensbilanz im ÖBH am Beispiel ABCAbwS & ABCABw, Sonderpublication ISBN: 978-3-902670-00-7, Vienna Gronau N. (2003): Modellieren von wissensintensiven Geschäftsprozesses mit der Beschreibungssprache KModeler, in Gronau N. Wissensmanagement: Potentiale – Konzepte - Werkzeuge, GITO Verlag, p3-30 Gronau, N. Bahrs, J, (2005): Prozessorientiertes Wissensmangement – Strategien, Methoden, Erfahrungen und Werkzeuge -, GITO Verlag, Berlin Heisig, P., Orth, R. (2007): Knowledge Management Framework. An international comparative Study Berlin Cambridge: Eureki 2007 ISBN 978-3-00-020575-0 Hinkelmann, K., Karagiannis, D., Telesko, R. (2002): PROMOTE - Methodologie und Werkzeug zum geschäftsprozessorientierten Wissensmanagement. In: Geschäftsprozessorientiertes Wissensmanagement, Springer-Verlag INSEMTIVES EU-Project (2012), http://www.insemtives.org/, Access: 07.03.2012 Karagiannis, D. and Telesko, R. (2000): The EU-Project PROMOTE: A Process-oriented Approach for Knowledge Management, Proceedings of the 3rd International Conference on Practical Aspects of Knowledge Management, Basel Karagiannis, D.; Kühn, H. (2002): Metamodelling Platforms. Invited Paper. In: Bauknecht, K.; Min Tjoa, A.; Quirchmayer, G. (Eds.): EC-Web– Dexa 2002, France, LNCS 2455, Springer-Verlag, Berlin, p. 182. Karagiannis, D., Höfferer, P. (2006): Metamodels in Action: An overview, In: J. Filipe & B. Shishkov & M. Helfert (Eds.), First International Conference on Software and Data Technologies:IS27-36. Setúbal: Insticc Press Karagiannis, D.; Utz, W., Woitsch, R. and Leutgeb, A. (2008): Business Processes and Rules: An eGovernment Case-Study. In: Hinkelmann, K. and Thönssen, B.: AAAI Spring Symposium AI Meets Business Rules and Process Management. Stanford, University, California Karagiannis D., Woitsch R. (2010), Knowledge Engineering in Business Process Management, In vom Brocke J., Rosemann M., (eds.) Handboork on Business Process Management 2, International Handbooks on Information Systems. Springer-Verlag Berlin Heidelberg KIWI EU-Project (2012), http://www.kiwi-project.eu/, Access: 07.03.2012 Kotler, P., Bliemel, F., (2001) Marketing-Management, 10. Aufl., Schäffer-Poeschel, Stuttgart, S. 102 Kühn, H., Bayer, F., Junginger, S., Karagiannis, D. (2003): enterprise model integration. In proceedings of the 4th
international conference Dexa 2003, Czech Republic, lncs 2738, Springer-Verlag, Berlin, pp. 379-392. Kühn, H., (2004): PhD Thesis: Methodenintegration im Business Engineering, University of Vienna MATURE D1.1 (2009): Results of the Ethnographic Study and Conceptual Knowledge Maturing Model METEOR-S (2012), Semantic Web Services and Processes, http://lsdis.cs.uga.edu/projects/meteors/index.php?page=1, access: 07.03.2012 Murzek, M. (2008). The Model Morphing Approach Horizontal Transformation of Business Process Models, PhD Thesis OMG (2012), Object Management Group, Meta Object Facility (MOF), http://www.omg.org/mof/, Access: 07.03.2012 OMI (2012) Open Models Initiative, www.openmodels.at, Access: 07.03.2012 OntoRules EU-Project (2012), http://ontorule-project.eu/, Access: 07.03.2012 OWL (2012), http://www.w3.org/TR/owl2-overview/, Access: 07.03.2012 plugIT (2012) EU-project, www.plug-it.org, Access: 07.03.2012 PROMOTE (2012), Modelling Language Specification, http://83.65.190.85/promote_repository/papers/PROMO TE%20Modelling%20Language%20Specification.pdf, Access: 07.03.2012 Schubert. L., Kipp, A. Wesner, S. (2008): "Supporting Collaborative Engineering Using an Intelligent Web Service Middleware", Journal of Advanced Engineering Informatics, vol. 22, num. 4 SEKT EU-Project (2012), http://www.sekt-project.com/, Access: 07.03.2012 Strahringer, S. (1996): Metamodellierung als Instrument des Methodenvergleichs: eine Evaluierung am Beispiel objektorientierter Analysemethoden. Shaker, Aachen, SUPER EU-Project (2012), http://www.ip-super.org/, Access: 07.03.2012 Valente, A and Housel, T. (2001): A Framework to Analyze and Compare Knowledge Management Tools, In Proceedings of the Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, IOS Press, Ohmsha Woitsch, R. (2004): PhD Thesis, Process Oriented Knowledge Management: A Service-Based Approach, PhD thesis University of Vienna Woitsch R., Karagiannis D., Fill H.-G. Blazevic V. (2008): Semantic Based Knowledge Flow System in Euopean Home Textile: A Process Oriented Approach with PROMOTE, I-Know, Graz Woitsch R., Leutgeb A. (2009): The BREIN-Roadmap with PROMOTE: A Use-Case Scenario of a Service-Based Knowledge Management Approach, I-Know, Graz WSMO, Web Service Modeling Ontology (2012), http://www.wsmo.org/, Access: 07.03.2012 W3C, OWL-S (2012): Semantic Markup for Web Services, http://www.w3.org/Submission/OWL-S/, Access: 07.03.2012
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