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Главная | Публикации | БИГ-СПб | Менеджмент знаний | From context to knowledge: consecutive mapping ontologies and contexts

From context to knowledge: consecutive mapping ontologies and contexts

Dmitry Kudryavtsev

Knowledge sharing, exchange and communication are critical tasks in any knowledge management or e-commerce initiative. In order to solve these tasks ontologies can be used. But there are certain communication problems with ontology-based knowledge sharing and exchange connected with context. The paper describes these communication problems, defines two contexts types and suggests methodical basis of solution for communication problems. This methodical basis is considered and factored in the following case study using consecutive mapping between different of context types and content ontology. This case study describes Knowledge Navigator – a map that relates contents of Formalized Management methodology with the corresponding context in order to reach effective knowledge communication to end users.

Keywords: ontology, context, mapping, matching, knowledge management.
Categories: H3.1, H3.3, H3.7, H5.0.

1.  Introduction

Knowledge sharing, exchange and communication are critical tasks in any knowledge management or e-commerce initiative. To support the sharing and exchange of knowledge both among information systems and people it is helpful to use ontology [Gruber, 1993]. With respect to human-computer interactions ontology often works as a representation, retrieval and navigation tool which structures knowledge objects. Now ontologies are already employed in portals, corporate memories, e-commerce and other knowledge management systems [Staab, 2001a, 2001b, Abecker, 1998, 2003]. Usually ontology specifies the Content of knowledge resources. Such ontology can be called Content ontology. In order to obtain the necessary knowledge objects a user should choose a specific notion from the content ontology and receive the respective knowledge objects (documents, parts of documents or experts) which were described by means of this notion.

2. Problem statement

There are two general problems that render ontology-based knowledge communication less efficient.
1. A Content ontology user is unable to set links between his/her task, problem, situation and notions in the Content ontology, thus he/she is unable to transform information into action.
2. A Content ontology user is unable to match his/her personal mental model with the notions in the Content ontology because of semantic and syntactical specialties of a person and ontology-creator.
These general problems can be specified for a specific real-life situation and can be demonstrated with the following example:

A management consulting company codified the experience of its consultants and received Formalized management methodology (“methodology” hereinafter in the paper) as a result. This methodology was further structured using content ontology which results in its being divided into topics. Despite this structuring it is rather hard to communicate the methodology because of the way the methodology can be used; its potential users and the methodology itself have their own specialties. These specialties can be viewed as communication problems and are as follows:

a. Different organizations that intend to use the methodology face different problems and tasks. Many problems and tasks do not require usage of every topic of methodology.

b. Implementing such a methodology is not a task faced by one person or a small group only; it requires a joint effort made by many persons employed in the organization. As a result the target audience for the methodology implemented is very broad and involves many people in management procedures (ranging from directors’ boards to linear managers). It is a subset of topics that is to be read and learned by a majority of users’ categories.

c. The core of the methodology integrates words quite unusual and new for the majority of Russian managers (Corporate / Enterprise Architecture, Business Engineering). In addition management research and practice have no conventional terms and concepts. Thus words and phrases used in implementing the methodology and especially in the topic headings can be misunderstood and users will be unable to set a relation between their mental models and topics of the methodology.
All these problems are related with the notion “context”. These problems make problematic effective knowledge sharing and communication. In order to solve these problems it is necessary to describe context and make explicit mapping between content ontology (or knowledge object directly) and context.

3. Context description

Context description and corresponding mapping methodologies depend on a definition of the notion “context”.

There are different definitions and types of context, but communication problems from [Section 2] comply with  the following two definitions:
Definition 1: Context is a description of knowledge application and creation conditions in terms of the enterprise ontology (e.g. organizational structure and the process models) [Abecker, 1998].

The design of the enterprise ontology (or context description) is built on insights and developments obtained from the enterprise ,  business process and organizational modeling in knowledge-based systems [Uschold, 1997]. Methodologies of mapping such a context and knowledge objects or content ontology are presented in [Abecker, 1998; 2003; Eppler, 2001, Vestal, 2005] and provide “pragmatical” relations (according to the semiotic model [Morris, 1938] pragmatics reflects relations between the signs and their users and creators).

Definition 2: Context is local (not shared) models that encode a party’s view of a domain [Ghidini, 2001].

The context contrasts with ontology in such a definition. According to [Bouquet, 2004] Ontologies are shared models of a domain that encodes a view which is common to a set of different parties. With respect to this context definition the mapping context and ontologies provide syntactic and semantic interoperability [Harmelen, 2005, Klein, 2001, Giunchiglia, 2003].

4. Mapping requirements for effective knowledge representation

Both context types defined earlier are necessary to solve the knowledge communication problems from [Section 2], i.e. to describe the context and map it with the content ontology. In order to distinguish these context types and set mapping requirements for effective knowledge representation respective working definitions are suggested for every type of context. According to the semiotic model it is possible to consider the context in Definition 1 as pragmatic context. However, the Context in Definition 2 will be termed and used in this paper as local context.

Pragmatic context can be either shared or not. Consequently the former is represented by the ontology and the latter is by a set of local contexts.

These definitions of contexts allow to state a methodical basis for solution of knowledge communication problems from [Section 1]. This methodical basis consists in mapping requirements for effective knowledge representation:

Requirement 1: Every ontology must be either shared by all the communication participants or be mapped with the corresponding local contexts of every participant (group of similar participants).

Requirement 2: Every knowledge object must be mapped with a pragmatic context (either directly or by means of the content ontology).

5. Case study: Knowledge Navigator

Knowledge Navigator (KN) is suggested as an end-user solution for knowledge communication problems from [Section 1]. The main objective of the KN is to map content ontology (with corresponding knowledge objects) and context. The Mapping requirements for effective knowledge representation (see [Section 4]) define the framework of KN which results in 3 components (Figure 1):

Figure 1. Knowledge Navigator Framework

1. Task-oriented navigator (“What for”-navigator)
It helps users to choose topics to solve certain tasks and problems of organization.
This navigator maps content ontology with Pragmatic context, which is represented in the form of Task Context ontology.

Table 1: Mapping content ontology and task context ontology

But although Task context ontology results from the analysis made by a consulting company and is shared by the authors, it is not shared by prospective users and consequently does not satisfy Requirement 1 from [Section 4]. In order to help the users identify their local problems every notion in Task Context ontology is mapped with a set of descriptive local task and problem contexts of users (see LC in Table 2). These local contexts are given even in user linguistics.  

Task 1: To change structures and business processes.

Descriptive local task and problem contexts for Task 1

LC 1

You feel the necessity to change organizational structure because it does not correspond to the business processes and market requirements.

LC 2

You feel that your company operation is inefficient, and you always encounter the same problems, for example, in processing your clients’ orders. “We either lose clients order, or we have many claims and nobody works with them, or incur costs because we bought non-appropriate raw materials. Such raw materials were bought because we initially planned another kind of production, but such a production plan is a result of a deficient sales plan.”

Task 2: To establish order

Descriptive local task and problem contexts for Task 2

LC 3

You might have encountered situations of complete chaos resulted from disorganization in your company. These cause the same problems to reoccur.

LC 4

The strategy issues are left unheeded in your company. The main question your company managers are faced with is “how to cater to the clients’ order”

Table 2: Mapping local task and problem contexts of users and task context ontology

Actually users of this navigator do two consecutive mappings, see Step 1 and Step 2 in Figure 2.

Figure 2. Task-oriented navigator - two consecutive mappings

Example for shaded blocks from Figure 2:
If you face situation similar to LC 1 or LC 2  then you need To establish order according to Table 2 (Step 1). Then according to Table 1 if you need To establish order:

  • learning topic Ideology of Modern Organization is useful for you
  • learning topic Business Engineering and Modeling is important for you
  • learning topic Corporate Architecture as a Control Object is useful for you
  • learning topic Tools of Business Engineering  is critical for you

Choice of these topics is a result of Step 2 from Figure 2.

2. Role-oriented navigator (“Who”-navigator)
It helps users to choose topics for learning with respect to their Roles in the organization.
This navigator maps content ontology with Pragmatic context, which is represented in the form of Role Context ontology, see Table 3.

Table 3: Mapping between content ontology and role context ontology

Similarly to task-oriented navigator, Role Context ontology is ambiguous and polysemantic for the users, because Roles (notions of Role Context ontology) can bear different responsibilities in different organizations. Thus the Role Context ontology is mapped with the elements derived from the next Pragmatic context — Activity Context ontology, see Table 4. The Activity Context ontology can be considered as shared by potential users, because all the management activities presented are typical for different organizations.

  • Setting corporate values and principles
  • Define the business concept and long-term vision
  • Develop business strategy
  • Choose and develop methods of management

CEO (Chief Executive Officer)

  • Perform external and internal analysis of business
  • Develop business strategy
  • Develop and set organizational goals

Director of Business Development

… other activities and roles.


Table 4: Mapping Role Context ontology and Activity Context ontology

Actually users of this navigator also do two consecutive mappings, see Step 1 and Step 2 in Figure 3.

Figure 3. Role-oriented navigator — two consecutive mappings

Example for shaded blocks from Figure 2:

If you Perform external and internal analysis of business, Develop business strategy and Develop and set organizational goals then according to Table 4 your responsibilities correspond to the role of Director of Business Development based on the consultants’ experience. (Step 1). Then according to Table 3 if you comply with this type of a role then:

  • learning topic Ideology of Modern Organization is critical for you
  • learning topic Business Engineering and Modeling is important for you
  • learning topic Corporate Architecture as a Control Object is useful for you

Choice of these topics results from Step 2 of Figure 3.

3. Semantic navigator (“What about”-navigator)
This navigator helps users to relate topics in authors’ language with their knowledge and thus refines a subset of topics to learn.

This navigator maps the Content ontology with the Local Content Contexts, which are represented by the keywords.

6. Conclusions and future work

This paper suggested the requirements for effective knowledge representation based on mapping between ontologies and context with respect to two types. It described a solution for real-life knowledge communication task called Knowledge Navigator. This solution illustrated consecutive mapping between ontologies and contexts – mapping which was necessary to effectively communicate knowledge to different users, which solve different tasks and have different understanding of domain and background.

This paper represents work–in–progress. The next steps in this work include: establishing and obtaining a feedback on the Knowledge Navigator from the end-users; transition of the methodology described above together with KN from the book form into an electronic environment.

7. References

[Abecker, 1998] Abecker A., Bernardi A., Hinkelmann K., Kuhn O., Sintek M. Toward a Technology for Organizational Memories IEEE Intelligent Systems. – 1998. - №3, 40-48.
[Abecker, 2003] Abecker, A., D. Apostolou, W. Maas, G. Mentzas, C. Reuschling, S. Tabor Towards an Information Ontology for Knowledge Asset Trading Presented at the ICE 2003 - 9th International Conference of Concurrent Enterprising, Espoo, Finland, 16-18 June 2003
[Bouquet, 2004] Paolo Bouquet, Fausto Giunchiglia, Frank van Harmelen, Luciano Serafini, Heiner Stuckenschmidt, Contextualizing Ontologies, Journal of Web Semantics, 2004, Vol.1, №4. 
[Eppler, 2001] Martin J. Eppler Making Knowledge Visible Through Intranet Knowledge Maps: Concepts, Elements, Cases Proceedings of the 34th Hawaii International Conference on System Sciences - 2001
[Harmelen, 2005] Frank van Harmelen Ontology Mapping: A Way Out of the Medical Tower of Babel? AIME 2005,  pp. 1–4, 2005.
[Gruber, 1993] Gruber T. A translation approach to portable ontology specifications. Knowledge Acquisition, 1993, Vol. 5, 199- 220.
[Ghidini, 2001] C. Ghidini, F. Giunchiglia, Local models semantics, or contextual reasoning = locality + compatibility, Artif. Intell. 127 2 (2001) 221–259.
[Giunchiglia, 2003] Giunchiglia F., Shvaiko P. Semantic Matching.  In The Knowledge Engineering Review  Journal, vol. 18(3), pp. 265-280, 2003.
[Klein, 2001] Klein M. Combining and relating ontologies: an analysis of problems and solutions, Workshop on Ontologies and Information Sharing, IJCAI'01, 2001, №4-5.
[Morris, 1938] "Foundations of the Theory of Signs." International Encyclopedia of Unified Science, ed. Otto Neurath, vol. 1 no. 2. (Chicago: University of Chicago Press, 1938. Rpt, Chicago: University of Chicago Press, 1970-71).
[Staab, 2001a] Steffen Staab, Alexander Maedche Knowledge Portals: Ontologies at Work. AI Magazine 2001, Vol. 22, №2, p. 63-75.
[Staab, 2001b]  Staab, S., Schnurr, H.-P., Studer, R., Sure, Y. Knowledge processes and ontologies, IEEE Intelligent Systems, 2001, Vol. 16 No.1, pp.26-34.
[Uschold, 1997] Uschold M., King M., Moralee S. and Zorgios Y. The Enterprise Ontology AIAI, The University of Edinburgh, 1997.
[Vestal, 2005] Wesley Vestal Knowledge Mapping: The Essentials for Success APQC: Publications. 2005.

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