=Paper=
{{Paper
|id=Vol-3223/paper2
|storemode=property
|title=Map of the Maps: Conceptualization of the Knowledge Maps
|pdfUrl=https://ceur-ws.org/Vol-3223/paper2.pdf
|volume=Vol-3223
|authors=Dmitry Kudryavtsev,Tatiana Gavrilova,Elvira Grinberg ,Miroslav Kubelskiy
|dblpUrl=https://dblp.org/rec/conf/bir/KudryavtsevGGK22
}}
==Map of the Maps: Conceptualization of the Knowledge Maps==
https://ceur-ws.org/Vol-3223/paper2.pdf
Map of the Maps: Conceptualization of the Knowledge Maps
Dmitry Kudryavtsev 1, Tatiana Gavrilova2, Elvira Grinberg 3 and Miroslav Kubelskiy 4
1
Digital City Planner Oy, Helsinki, Finland
2
Graduate School of Management SPbU, Saint-Petersburg, Russia
3
Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia
4
DataFabric Ltd, Saint-Petersburg, Russia
Abstract
The company’s knowledge assets and ability to coordinate them become increasingly critical.
Knowledge maps can scaffold knowledge search and decision-making by creating visible links
between knowledge, its application, source, and owner. The term Knowledge map was used
with various meanings in several scientific communities: knowledge management, education
studies, organization studies, decision analysis, artificial intelligence, etc.
The term itself is still rather vague. To generalize the variety of the definitions, one can use the
term knowledge map as a description of the sources, flows, owners, and application areas of
knowledge within the organization. The current study considers knowledge maps from a
knowledge management perspective and is devoted to generalizing the numerous scattered
knowledge mapping research and practices. It aims to identify and consolidate knowledge
maps interpretations by analyzing their contents.
The discussed research results in developing the ontology of knowledge maps. Existing types
of knowledge maps are associated with fragments of this ontology. The such ontology may
help select the necessary building blocks (templates and elements) for knowledge mapping in
specific companies and situations. The research methodology is based on literature review,
semantic analysis, and ontological engineering. The paper also provides the knowledge
mapping ontology application guidelines and demonstrates them via a case study.
Keywords 1
Knowledge maps, Templates, Ontology, Semantics, Knowledge management systems
1. Introduction
Modern industry, educational, and research organizations are burdened under data/knowledge flow.
In light of the knowledge age, information overload research in the business context rises rapidly in
many fields with various frameworks, methods, and subjects [1, 2]. This paper studies knowledge maps
– special tools for knowledge analysis that can improve managerial decision-making and reduce the
cognitive workload of knowledge workers. The knowledge maps can scaffold the knowledge search
and decision-making by specifying what is known in the organization, where this knowledge is located,
who knows what, and what for a company requires specific knowledge.
Since a knowledge map is usually a visual representation, it often links knowledge structure
diagrams with other organizational diagrams and charts. It is the basis for determining knowledge
commonality or areas where similar knowledge is used across multiple processes. The knowledge map
as a “description of the sources, flows, constraints and sinks of knowledge within the organization” [3]
can support the knowledge workers and decision-makers [4].
BIR 2022 Workshops and Doctoral Consortium, 21st International Conference on Perspectives in Business Informatics Research (BIR 2022),
September 20-23, 2022, Rostock, Germany
EMAIL: dmitry.kudryavtsev@digicityplanner.com (A. 1); gavrilova@gsom.spbu.ru (A. 2); elviramitim@gmail.com (A. 3);
miro@datafabric.cc (A. 4)
ORCID: 0000-0002-1798-5809 (A. 1); 0000-0003-1466-8100 (A. 2); 0000-0001-8871-1349 (A. 3); 0000-0002-9005-0938 (A. 4)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
14
The current study considers knowledge maps from a knowledge management perspective and is
devoted to generalizing the numerous scattered knowledge mapping research and practices.
“Knowledge management perspective” means that we do not include into the term “knowledge map”
different knowledge diagrams – like mind maps, concept maps, flowcharts, etc. that depict the primary
relations between the pieces of knowledge body.
The discussed research results in a conceptual model or ontology [5] for knowledge maps used in
knowledge management. Such an ontology may help select the necessary elements for knowledge
mapping in a specific company and situation.
Research in knowledge mapping is not so young and started in the late 90-ies. There are some
generalizing works in this area, for example, an overview of the concept [6, 7], a classification of
knowledge maps using different criteria (by purpose, by format, by content) [8], a systematic review of
knowledge mapping research [9]. There are also some methodologies and frameworks for knowledge
map’s structure and templates [10] (American Productivity & Quality Center). But there are no surveys
devoted to the content ("what") of knowledge maps, which analyze typologies, examples, and templates
of knowledge maps to identify possible components and building blocks of a knowledge map. This
research fills the gap mentioned above.
2. Literature review
2.1. Knowledge maps typologies
Several different typologies of knowledge maps have arisen during the last two decades. The early
developed ones have inconsistent classification parameters or describe only a tiny part of the knowledge
maps variety. Further, we focus only on several mature classifications – we took Eppler’s typologies
[7, 8] since they are highly cited in the academic community and APQC’s typology [10, 11], since
APQC’s approach to knowledge management and mapping is rather famous and used in industry.
The classic of visual approach to knowledge management Martin J. Eppler proposes several
extensive classifications of knowledge maps. A practical one [7] is:
1. Knowledge source maps (where the knowledge is),
2. Knowledge asset maps (what kind of knowledge we have),
3. Knowledge structure maps (how the knowledge is organized and interconnected),
4. Knowledge application maps (which knowledge is needed for performing activities, producing
required results, and achieving goals),
5. Knowledge development maps (how certain knowledge is developed).
In his later work, Martin Eppler suggested a broader classification [8] which is more precise, rigid,
and formal. That classification is multi-faceted as it includes several simple classifications, which are
created based on different classification principles or facets:
1. by intended purpose or knowledge management process (‘‘why?’’), which includes knowledge
creation maps, knowledge assessment or audit maps, knowledge identification maps, etc.
2. by content, which is represented in the map (‘‘what?’’): by content format (blogs, books,
repositories, online courses, etc.) and content type (methods, experts, processes, departments, lessons
learned, etc.
3. by graphic form for representing the map (‘‘how?’’): includes all spectrum of visuals from
matrixes to metaphors
4. by their creation method (‘‘how?’’ and ‘‘who?’’) - from community-generated maps that are
constantly revised by map users to automatically generated maps;
5. by the level at which the map is applied (‘‘who?’’): personal, dyadic, team, departmental,
community, organizational, and inter-organizational levels in the enterprise.
APQC classification focuses on the organizational level and distinguishes enterprise, process- and
role-based levels for knowledge map application [11]. APQC classification of knowledge maps focuses
on the organizational level and distinguishes enterprise, cross-functional, process- and role-based levels
for knowledge map application. At the enterprise level APQC recommends focusing on the fit between
strategic goals and organizational knowledge using Strategic Overview Map and on knowledge at risk
in Expertise Overview Map. At the next level, cross-functional, the maps help to identify knowledge
15
(Expertise tacit map) and to understand potentials and gaps (Technical/Functional knowledge map).
Lastly, at the process- and role-level APQC suggests identifying needs and sources of knowledge via
Process-based map and Job/Role-based map. Another type of knowledge map at this level is
Competency/Learning needs map. As the title implies, this map outlines the learning needs associated
with a business process or job role.
These typologies [7, 8, 10, 11] provided a sufficient and representative amount of knowledge map
types for further analysis. Descriptions of these types were used for the analysis of their content.
The major part of these maps is presenting practical KM applications [12].
2.2. Ontology-based knowledge maps
Ontological analysis within the knowledge mapping context was used by a set of researchers [13,
14]. They integrated knowledge maps with process maps in health care via ontology engineering and
applied the who-what-why categorization of knowledge map instances together with the organizational
ontology for process reengineering. In both cases, the authors have chosen a certain set of knowledge
maps that match their main research purpose and didn’t provide an overview of existing knowledge
mapping templates. While the practical usefulness of ontological decomposition of knowledge maps
was demonstrated, research was still needed to study a variety of knowledge maps in use.
2.3. Knowledge modeling from an enterprise modeling perspective
There are also a lot of frameworks for modeling knowledge, which were suggested within the
enterprise modeling community. These frameworks and methodologies are compared in [15] and can
be divided into business process-oriented approaches, knowledge work-oriented approaches, agent-
oriented approaches, and holistic approaches. Typical objects for knowledge modeling within the
enterprise modeling research: knowledge and its status, organizational processes, types of knowledge
flows, and knowledge management enablers. Such a research stream may complement the current paper
but does not clarify the meaning of knowledge mapping.
3. Research methodology
Our research methodology is based on literature review, semantic analysis, and ontological
engineering. Existing typologies, templates, and examples of organizational knowledge maps from
literature are analyzed using semantic analysis – the main concepts, which are represented in knowledge
maps, and relationships between them are extracted. Methods of ontology engineering are used for
establishing a unifying ontology (conceptual model) for knowledge maps.
The research process is presented in Fig. 1.
The research started with a literature review, which was focused on the analysis of existing
typologies of knowledge maps (Section 2). This analysis helped to select knowledge map examples and
templates for further specification. The literature review was the main method at this step.
This analysis provided input information for ontology development (Section 4), which combined
ontology requirements specification via competency questions [16] with pattern-based ontology design
[17]. Knowledge map typologies provided the basis for competency questions (Section 4.1), while the
selected examples and templates were used for creating the associated information models, which were
used as content ontology patterns (Section 4.2). These competency questions and content patterns
helped to suggest ontology (Section 4.3). The paper also provides the guidelines for knowledge
mapping ontology application and demonstrates them via a case study – see Section 5.
16
Figure 1. The research process of the study
4. Knowledge maps ontology development
4.1. Competency questions
The analysis of different types of knowledge maps helped specify questions that may be answered
using knowledge maps. These questions can be considered competency questions, which define
functional requirements for ontology engineering:
1. What kind of knowledge does a company have?
2. Where is knowledge used and what knowledge is needed?
3. What are the sources of knowledge?
4. How can knowledge be assessed?
5. How is knowledge exchanged?
6. How is knowledge created/acquired/developed?
4.2. Information models of knowledge maps content
Specifications of content for the selected knowledge map examples and templates – information
models – can be considered as content ontology design patterns (“content patterns”) for creating an
ontology of knowledge maps. Fig. 2 demonstrates an example of the information model, which specifies
the content categories of the knowledge source map. The exemplary map (Fig. 2a) has several
categories: role, location, and competency area. The interrelations between those categories as well as
an example for one person are presented in Fig.2 b.
Fig. 2 represents a building block of an ontology of knowledge maps. High-level groups of concepts
are aligned with competency questions. Similar information models were developed for other types of
maps mentioned in Section 2.1; after that the pattern-based ontology design was applied to unite them.
17
a: Example of knowledge source map [7]
b: Information model for the knowledge source map (created by authors)
Figure 2. Creation of information models for knowledge map examples
4.3. Knowledge maps ontology
According to Presutti [17] the idea of pattern-based design implies that “the ontology project is
divided into the problem and solution spaces. The problem space contains a set of requirements (see
Competency questions), while the solution space contains a set of ontology-design patterns. The two
spaces are compared in order to identify patterns matching the requirements.” Then relevant patterns
are selected and combined.
Figure 3 generalizes the above-described information models of different knowledge maps (content
ontology patterns) into a cohesive ontology. The ontology represents four broad areas: knowledge itself,
its application, sources, and assessment. Each of the above-mentioned areas gives answers to one of the
general competency questions: what kind of knowledge does a company have; where is it located; etc.
A detailed set of notions and relations is given inside each of them. A list of synonymous verbs is given
for some types of relations (e.g. between Knowledge and Knowledge Sources).
This ontology gives a broader and deeper understanding of knowledge map content and design.
More specifically, the ontology can be used for defining the content of existing knowledge maps –
based on the presented ontology the overlaps of concepts were identified and reflected in Table 1. This
ontology-based specification of knowledge maps can be considered as a navigator which helps to
identify relevant knowledge mapping templates for describing certain content or to support the analysis
of existing knowledge mapping templates.
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Figure 3. Informal knowledge maps ontology
This ontology was further represented in the ontology editor – see fig. 4 and 5.
Figure 4. Taxonomy of classes in the knowledge maps ontology
19
Figure 5. A fragment of relationships between classes in the knowledge maps ontology
Table 1
Ontology-based specification of knowledge maps content (fragment)
Cross-functional Knowledge map
Knowledge-assessment or audit
Knowledge application map [7]
Process-Based Knowledge map
Knowledge-identification map
Strategic overview knowledge
Knowledge structure map [7]
Knowledge source map [7]
Knowledge-assets map [7]
Job / role based map [10]
Knowledge
mapping
templates
map [10]
map [8]
Concepts of the
[10]
[11]
[8]
knowledge map ontology
Knowledge (assets, elements) + + + + + + + + + +
Tacit or Explicit + + +
Knowledge source types + + + + + + +
Repository + +
Information system + +
Person (Expert) + + +
Organizational Role + +
Community +
Organizational unit + + +
Geographical location + +
Knowledge application area + + + +
Strategic focus area or goal +
Product and/or Service + +
Process and/or Function + + + +
Organizational Role +
Knowledge assessment + + + +
Priority or Importance + +
Level of expertise + +
Required (needed) knowledge + +
Current knowledge +
Knowledge gap + +
20
5. Knowledge maps ontology application
5.1. Approach for the knowledge maps ontology application
We propose the following knowledge maps design process for companies, which is based on the
suggested ontology, competency questions, and the table with knowledge maps specifications:
1. Define the goals/purpose of the knowledge map application
2. Select relevant competency questions from the list in section 4.1
3. Identify the necessary ontology elements – see section 4.3
4. Identify possible knowledge map types and templates for reuse via table 1
5. Design company-specific knowledge map template/-s
5.2. Example of the knowledge maps ontology application
Our team has applied the above-described approach in several knowledge-intensive organizations.
The main challenge that we faced in knowledge mapping was the absence of knowledge domain
structure. One of the cases is described further in more detail as it follows step by step the suggested
methodology.
1. Purpose was stated by the company manager in a broad way: to identify and evaluate existing
knowledge assets in one subdivision. That subdivision comprises more than 300 people distributed
in eight cities and working in four different subject areas.
2. Competency questions were chosen from four abovementioned groups as follows:
a) What kind of knowledge does a company have?
b) What are the sources of knowledge?
c) Where is knowledge used and what knowledge is needed?
d) How can knowledge be assessed?
Moreover, to identify knowledge exchange auxiliary question was introduced:
3. Ontology elements. A broad set of ontology elements from Table 1 was mapped:
a) Knowledge
b) Knowledge sources: Person and Role
c) Knowledge application: Process and/or Function, Product and/or Service
d) Knowledge assessment: Level of expertise /Current knowledge
4. Templates for reuse. According to Table 1, three main templates were chosen for reuse and
adaptation:
Knowledge source map, Knowledge assets map and Process-based knowledge map
5. Company-specific knowledge maps. The large questionnaire produced rich data for the mapping.
It was represented in a bundle of interactive knowledge maps rather than one static picture. That
bundle includes but is not limited to the maps described further.
Table 2. Roles and knowledge domains map
Knowledge domain
Explorative
Digital Petro- geology
Sedimentology
technologies physics
Department Role Name
Specialist of petrophysics Anna 5
Data analysis Lab worker Viktor 4 2 1
Expert Olga 5 5
Specialist of 3D modelling Andrew 5 2 3
21
The second view, the “Process resource map” (see table 3) is a managerial tool for strategizing and
project planning. It represents the number of knowledge owners and their level of expertise.
Table 3. Process resource map
Andrew
#of
Viktor
Anna
Olga
…
Process Actions knowledge
owners
Data quality control 2 4 1
Structural data
5 5 5
interpretation
Seismic data
interpretation Dynamic data
7 2 5 5
interpretation
Calculation of
11 4 5 3
geological uncertainties
6. Conclusion
Professional knowledge maps help to identify intellectual capital, socialize new members, enhance
organizational learning, and help anticipate impending threats and opportunities [6]. Knowledge
mapping makes feasible the process of continuously evolving organizational memory, capturing and
integrating strategic explicit knowledge [18]. This process takes place within a company or institution
and between it and its external environment [18]. Knowledge mapping may also support the learning
within the company as it enhances and enriches the visibility of knowledge structure.
This paper and designed ontology clarify the theory and practical content of knowledge maps, their
meaning, and possible components. It provides building blocks for creating company-specific
knowledge mapping templates and practices. In the context of the digital era, the created ontology may
improve the design of an information system and integration of this system into overall organizational
IT architecture [18, 19].
The presented ontology of knowledge maps can be used as an approach to develop an IT solution
for automating the knowledge map building and application using knowledge graphs [20]. The
knowledge map becomes a map of the maps by integrating the knowledge sources, patterns,
applications, and locations into one “big picture” combining and projecting different company business
maps.
7. Acknowledgements
The reported study was partially funded by RFBR, project number 20-07-00854.
8. References
[1] P. Roetzel, Information overload in the information age: a review of the literature from business
administration, business psychology, and related disciplines with a bibliometric approach and
framework development. Business research. 12, 479–522, 2019. https://doi.org/10.1007/s40685-
018-0069-z
22
[2] P. N. M. Kamal, N. Buniyamin, Knowledge management system in industries. In: 4th Int. Conf.on
Electrical, Electronics and System Engineering, 107–111. IEEE Press, 2018.
[3] J. Liebowitz, Linking social network analysis with the analytic hierarchy process for knowledge
mapping in organizations. Journal of knowledge management. 9, 76–86, 2005.
[4] F. Corea, AI knowledge map: How to classify AI technologies. In: An introduction to data,
Springer, Cham, 2019, pp. 25–29. https://doi.org/10.1007/978-3-030-04468-8_4
[5] T. Gruber, A translation approach to portable ontology specifications. Knowledge acquisition. 5,
199–220, 1993.
[6] M. Wexler, The who, what and why of knowledge mapping. J. of knowledge management. 5, 249–
264, 2001.
[7] M. Eppler, Making knowledge visible through knowledge maps: concepts, elements, cases. In:
Holsapple, C., ed..Handbook on KM, 1, 189–205. Springer, Heidelberg, 2004.
[8] M. Eppler, A process‐based classification of knowledge maps and application examples.
Knowledge and process Management. 15, 59–71, 2008.
[9] A. Balaid, Abd Rozan, et al., Knowledge maps: A systematic literature review and directions for
future research. Int. J. of Information Management. 36, 451–475, 2016.
[10] APQC. Knowledge Mapping Concepts and Tools, 2021. URL: https://www.apqc.org/resource-
library/resource-collection/knowledge-mapping-concepts-and-tools
[11] APQC. Getting Started with Knowledge Mapping, 2022. URL: https://www.apqc.org/resource-
library/resource-listing/getting-started-knowledge-mapping
[12] M. Harper, L. Trees, Knowledge mapping in action. How Organizations Map Their Knowledge
and Learn from the Results. In: An APQC best practices report, 2018. URL:
https://www.apqc.org/resource-library/resource-collection/knowledge-mapping-action
[13] G. Mansingh, K. Osei-Bryson, H. Reichgelt, Building ontology-based knowledge maps to assist
knowledge process outsourcing decisions. Knowledge Management Research & Practice, 7(1), 37-
51, 2009.
[14] L. Rao, G. Mansingh, K. Osei-Bryson, Building ontology based knowledge maps to assist business
process re-engineering. Decision Support Systems, 52(3), 577-589, 2012.
[15] D. Kudryavtsev, D. Sadykova, Towards architecting a knowledge management system. In: IFIP
Working Conf.on The Practice of Enterprise Modeling, 36–50. Springer, 2019.
[16] M. Suárez-Figueroa et al, Introduction: Ontology engineering in a networked world. In: Ontology
engineering in a networked world, 1–6. Springer, Berlin, Heidelberg, 2012.
[17] V. Presutti, Pattern-based ontology design. In: Suárez-Figueroa, M.C. et al., eds. Ontology
Engineering in a Networked World, 35–64. Springer, Berlin, Heidelberg, 2012.
[18] H. Xin, The Application of Knowledge Map and the Construction of Enterprise Knowledge Map.
In: Int. Conf. on Applications and Techniques in Cyber Security and Intelligence. 28–34. Springer,
Cham, 2021.
[19] D. Oberle, How ontologies benefit enterprise applications. Semantic Web.5, 473–491, 2014.
[20] A. Hogan, Knowledge graphs. Synthesis Lectures on Data, Semantics, and Knowledge. 54, 1–37,
2021. https://doi.org/10.2200/S01125ED1V01Y202109DSK022
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