Visualizing a dynamic knowledge map using Semantic Web technology

Hong-Gee Kim1, Christian Fillies2, Bob Smith3 and Dietmar Wikarski4

2 Semtation GmbH, Schwabach, Germany

3 Cal. State University, USA

4 University of Applied Sciences Brandenburg, Germany


Abstract. Visual knowledge[1] maps are being used to improve the communication processes within global organizations.  Knowledge maps are graphical presentations of ontological knowledge as well as of business processes. Especially for enterprises working in a multi-cultural space the explicit formalization of knowledge and business rules using graphical models seems to be a very promising approach in order to improve discussion and learning processes. Publishing and automatic inference or search techniques are becoming available due to the latest standards for semantic web worked out by W3C. This article gives an impression how to create end user interfaces for the ”Corporate Knowledge Base” using MS Office and Visio with the modeling tool SemTalk. We are discussing several problems on capturing and maintaining large scale knowledge bases. Specific attention is given to the problem of weighting and association of information from orthogonal ontologies, which arises while using the same concepts in different graphical scenarios.


1   Graphical representation of a knowledge map

Knowledge management in an organization is the ability to create business values and generate a competitive advantage. However, knowledge is not visible in its nature so that managing it is very difficult. Tacit knowledge embodied in the experiences of organizational members is easily lost unless it is transformed into a usable form. Knowledge mapping provides a framework for visualizing knowledge that can easily be examined, refined, and shared by non-expert knowledge users. A knowledge map can also be used as an interactive tool that links different conceptualizations of the world. 


Since people have their own concept of knowledge in terms of its form and content, meaningful communication is difficult especially when the number of communicative actors is large. There is a need to develop a common way of constructing and maintaining knowledge in a visual form [8]. Many methods of knowledge representation have already been developed and devised for AI applications. Most of these techniques are for machine-processed and target specific systems. In contrast, the application discussed in this paper provides a user-friendly method for developing a knowledge map that helps knowledge users visualize their implicit ontologies[2]  and workflows. Knowledge about the same object is represented differently depending on contexts. Since the visualizing tool proposed in this paper suits with the dynamic feature of a knowledge map, it helps people to modify and to combine ontologies across domains. SemTalk using semantic web technology equips the user with a method for knowledge representation that is not only machine understandable but human readable because it includes both graphical and textual forms of information.


Semantic nets are a powerful diagrammatic knowledge representation technique. Figure 1 is an example of a knowledge map that is represented in a semantic net. The knowledge mapping tool of SemTalk is more flexible and less constrained than semantic network systems in the sense that any graphical form of knowledge representation is possibly modeled including UML and Conceptual Graph. A knowledge map represents meaningful relationships between concepts in the form of propositions. A proposition represented in a knowledge map consists of two or more concepts linked by relational labels to form a semantic unit.



     Fig. 1. A knowledge map of plants

2   Context dependency of a knowledge map

People conceptualize their world differently. Accordingly, a knowledge map about the same object may contain different contents and structures depending on the contexts for which they are generated. For example, a scientist usually has a view that electrical ‘current’ is a kind of “constraint-based events”, but in some contexts can share with others the naïve view that it is a material substance. We can have multiple views for a single concept depending on context [10].


As an object has a huge number of properties, there are many ways of conceptualizing a given object, each serving a particular goal. The concept ‘car’ may contain different information for a car dealer, a manufacturer, a driver, and a cartoonist. We tend to conceptualize an object as having a certain set of properties in the context of the kind of things involved. For example, there are explanatory networks for a car’s fuel systems, known only to engineers, that consist of many mechanically defined terms unique to engineers. A cartoonist could also have similar clusters of terms for the shapes and motions of cars. An object is conceptualized and organized into ontology differently, yet some information can be shared across ontologies when it is needed [11]. For example, although the cartoonist’s ontology of ‘car’ does not have any mechanical information of a car, such information sometimes needs to be accessible to the cartoonist in a certain situation. Figure 2 depicts how the two knowledge maps are merged in terms of cross-ontological relationships.

Fig. 2. Ontology merge

Further information about merging of ontologies can found at [4].

3   Knowledge structure represented in a knowledge map

Knowledge maps are defined as “a graphical representation of the connections by the brain in the process of understanding facts about something” [7]. While the static side of knowledge mapping is to represent the connections between properties identified during conceptualization, the dynamic side stands for the process of inferring values on those properties in the problem solving or decision making contexts. Concepts can be either directly or by analogy transferred from one domain to another. For example, the use of the physical notion of transitory state can be transferred into the domain of certain business management problems. The dynamic aspect of knowledge mapping is used to improve the communication processes within global organizations. Especially for enterprises working in a multi-cultural space, the explicit formalization of knowledge and business rules using knowledge maps seems to be a very promising approach in order to improve communication and learning processes.


The actual knowledge does not have a static structure but is dynamically constructed by identifying and indexing pieces of information or knowledge components depending on contexts. Figure 3 describes how knowledge is represented in a knowledge map that shows the hierarchical structure of knowledge. Understanding is not just knowing an item of knowledge, but knowing how the supporting knowledge relates each higher knowledge item [8]. A measure of importance represents how important each supportive piece of knowledge item is to the higher one. The weighting in the association between knowledge components can vary depending on contexts. In the actual use of a knowledge map granularity is flexibly applied in the sense that a certain knowledge component, in this example ‘cooking chicken’, may consist of a deeper level knowledge map with a greater granularity.    




Fig. 3. Knowledge structure about cooking chicken  


4   A real world use case

Large and successful organizations today can afford to invest resources into formalizing corporate ontologies, but medium sized organizations can seldom afford the time and the resources to effectively execute these important projects.

Our use case is a 105 year old engineering and chemical testing lab employing over 100 people. The engineering and construction professionals have conflicts with the chemistry lab professionals. The IT department is very small and under-funded. They use WinWord and Excel on a network, and have used Visio for planning and training purposes in the past. SemTalk now offers a low cost approach to visualizing workflows and their implicit ontologies based on the W3C notation RDFS using Visio shapes which are relevant to their business problems.



Fig. 4. Knowledge structure for the chemistry testing lab  


An explicit examination of each group's ontology (and specialized jargon) significantly enhances the CEO/Owners' ability to more effectively balance resources within the organization. Applying an importance measure on the specific issues found in both organizational sub-structures helps to make communication deficits explicit. As a direct result a 20% increase in profit 6 months after project completion is expected.


5   SemTalk

SemTalk is being used in ontology projects helping people to agree on a common language. As described in Fillies et al. [5] those graphical ontologies may be used in several ways such as terminology control for technical writers. Ontologies represented in an application independent XML based format are an important building block for any knowledge management system, for business process modeling and for the consistent definition of large projects e.g. using MS Project. Ontology based business process models can be maintained, translated and reused with significantly less effort than conventional process models. This especially applies for process models describing web services.

5.1   Architecture of SemTalk

SemTalk does work on an RDF(S)-like XML data structure. Diagramming information and object oriented features like methods and states have been added to RDF(S). It also has an optimized structure for basic inferences as inheritance and graph traversals. There is an object engine providing a COM API in order to be able to use the engine within MS Office products. For the graphical presentation of models we have used MS Visio for two reasons: (i) the tool is widely used in industry, therefore people are used to it and (ii) it is easily extensible through an API.


Fig. 5. Architecture of SemTalk  

The SemTalk object engine is used to define semantics - in other words a Meta Model - for existing Visio shapes. You can graphically define which shapes are allowed to be connected with each other. SemTalk supplies the infrastructure to define complete modeling methods inside Visio. Those methods are e.g. for DAML [3], for Enterprise Resource Planning (ERP) product modeling and for Business Process Modelling (BPM) methods. SemTalk has a couple of interfaces to CASE tools like Rational Rose and to BPM tools. There is a simple report generator that creates HTML tables by using XSL for formatting.

5.2   Notation for Semantic Nets

In respect to the very broad audience we want people to be able to read our models without learning a notation. We have best experiences using the very simple bubble notation, shown in some of the pictures below. It is important to label most of the links and not to use graphical encodings which are known from graphical languages as Entity Relationship diagrams.

For readers with a technical background more complex notation with various shape types can be used. Examples are the DAML Notation and e.g. a user interface for a product configuration engine.

One of the great advantages of using Visio is that is contains a large collection of predefined and extendable shapes. The shapes correspond quite natural to classes. Using pictures improves the acceptance of the models which is an important success factor in Knowledge Management.  

5.3   Referencing external Knowledge Bases

WordNet®, which was developed by the Cognitive Science Laboratory at Princeton University under the direction of Professor George A. Miller, is a huge online lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets. SemTalk uses WordNet via Dan Brickley’s RDF(S) web service for WordNet 1.6 [2] [13].


Fig. 6. A small vehicle model built from WordNet


The models are being built with external model repositories incrementally. Once you have used a class name in a model you can look for related objects in external repositories and integrate them into your model (Figure 6). The idea of using an external glossary basically ensures that people are talking about the same thing with a well defined Uniform Resource Name (URN) to identify objects and related hyperlinks to access their definitions. The other benefit users have from such ontologies is that they are getting hints for related objects or subclasses to use in the model.

The objects remember their origin and can be refreshed (or replicated) from their external data source once the source has changed. In a very similar way you can link one class to another class living in an external model which was created using SemTalk and which is published on a web server. This technology results in a web of hyperlinked models based on RDF(S) as a common standard.

6   Weighted Knowledge Maps in SemTalk

Cross ontology integration is a very common problem which arises as soon as multiple organizational units such as different departments within one company are involved. It becomes very important once business partners with a very inhomogeneous cultural background and communication strategies are being forced to solve real world problems together. This is especially the case if corporations from Asia move to western markets or vice versa.


Abstract and graphical models for knowledge and business processes have been used from the very first days of mankind to ease communication. SemTalk is a modeling tool designed to create knowledge structures in the semantic web format RDFS (Resource Description Framework, [1]. The semantic web is a kind of a distributed world wide knowledge model. One of the basic ideas of the semantic web is to denote concepts of discourse by URN. Once a group of users have has agreed that they are talking about the same topic, they can refer to it from their specific application models by a public URN. This technique is used to disambiguate words by explicitly mentioning homonyms and assigning synonyms to concepts. Beyond using URNs and synonyms, SemTalk relies on the use of manually clustering of information on diagrams, contexts or scenarios. Each object and a subset of its associations can participate in multiple scenarios. The technique of weighting the importance of an association in a specific context offers first of all additional information. For larger projects the importance factor of associations helps to reuse the object in contexts build by different people, because a statement made in one context may be less important in other contexts.



Fig. 7. Using line width to visualize importance

A very simple but effective way to visualize importance is to use graphical properties such as line width or no node size in order to emphasize specific aspects of the scenario.

Adding weighting and importance factors to the RDFS class model was possible because of two reasons: 1. RDFS is based on XML and 2. from the tool builders point of view because SemTalk has an open meta model, which allows the extension of the association (RDF speech: “Property”) and regards them as first class objects.


SemTalk is integrated in MS-Office. It has a Visio based graphical user interface which makes it easy to use for a broad range of users. Using Office XP SmartTag technology, semantic web glossaries can be used from all MS Office applications to lookup words in an ontology or process model.

7   Future Research

In this paper we have shown how to apply dynamic knowledge maps to Semantic Webs mainly for the purpose of improving communication and understanding between human readers of models. The semantic Web also has an important influence for the communication of programs or machines. Interpreting process descriptions by workflow engines or executing processes with MS Project having a (fuzzy) measure of “importance” has to be investigated.


1.  Berners-Lee, T. Hendler, J., and Lassila, O.: published an article about the Semantic Web in Scientific American. "A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities", See

2.  Brickley, D.: RDF(S) web service for WordNet 1.6, cf.

3.  Darpa Agent Markup Language (DAML): cf.

4.  Doan, A., Madhavan, J., Domingos, P., Halevy, A.: “Learning to Map between Ontologies on the Semantic Web”, WWW2002, May 7-11, 2002, Honolulu, Hawaii, USA ACM 1-58113-449-5/02/0005

5.  Fillies, C., Wood-Albrecht, G., Weichhardt, F.: A Pragmatic Application of the Semantic Web Using SemTalk, WWW2002, May 7-11, 2002, Honolulu, Hawaii, USA ACM 1-5811-449-5/02/0005

6.  Flynn, J.: DAML Visio Shapes, cf.

7.  Gomez, A., Moreno, A., Pazos, J., Sierra-Alonso, A.: Knowledge maps: An essential technique for conceptualization, Data & Knowledge Engineering 33. (2000) 169-190

8.  Gordon, J.L.: Creating knowledge maps by exploiting dependent relationships, Knowledge-Based Systems 13. (2000) 71-79

9.  Gruber: (2001)

10. Kim, H.-G.: “A Psychologically Plausible Logical Model of Conceptualization.” Minds and Machines, 7. (1997) 249-267

11. Kim, H.-G.: “Formalizing Perspectival Defeasible Reasoning.” Proceedings of the 30th Hawaii International Conference on System Science, Vol. V. (1997) 347-353

12. W3C: RDF Schema Specification., 1999, O. Lassila and R. Swick: Resource description framework (RDF), model and syntax specification Technical report, W3C, 1999, W3C Recommendation

13.WordNet: cf.

[1] This research was partially supported by Brain Science and Engineering Research Program sponsored by Korean Ministry of Science and Technology

[2] We are using the term “Ontology” here in the sense explained by Tom Gruber: “In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set-of-concept-definitions, but more general. And it is certainly a different sense of the word than its use in philosophy.” [9]