Building Atlas of Knowledge Maps: Towards Smarter Collaboration

Anna Kuznetsova

1 a

, Tatiana Gavrilova

2 b

and Olga Alkanova

2 c

Lyceum No. 408, St. Petersburg, Russian Federation

Graduate School of Management, St. Petersburg State University, St. Petersburg, Russian Federation

Keywords: Ontology, Knowledge Maps, Knowledge Atlas, Visualization System, Knowledge Transfer.

Abstract: The paper discusses the possibilities and prospects for creating corporate atlas of knowledge maps – a visual

guide of diagrams describing the intellectual assets of the enterprise. The discussed case is based on the

university business school. Mapping or visualization provides information transparency of communications

in universities making collaboration smart and effective. The walls of universities are opaque, and

visualization provide a higher level of teaching, research, consulting and administration. The paper presents

the preliminary results of the project “Methodology and technology for developing digital knowledge maps

for education and research teams’’ and proposes and describes specific features of a systematic repository of

diagrams, that is called an atlas of knowledge maps. We developed a set of diagrams to describe knowledge,

created an ontology of the properties of such maps and suggested considering the most popular ones as a kind

of atlas from which decision makers can select relevant maps for their work. The survey is preceded by the

use of ontologies - conceptual models of areas of knowledge and professional activities of the teacher. In

general, the approach can be adapted to business companies and government organizations if they are

interested in disclosing their intellectual capital.

1 INTRODUCTION

Business and academic work require cooperation.

Learning includes access to influencers and experts.

It can be difficult to find colleagues and potential

partners in an overloaded world of redundant and

contradictory information.

But companies and universities are in no hurry to

share their intellectual assets, and often companies

themselves do not know about their "treasures".

Acquisition and systematization of such information

resources are useful primarily for the companies

themselves, in addition, they are invaluable in the

market. The paper discusses the possibilities and

prospects for creating the atlas of corporate

knowledge maps - visual guide to the intellectual

assets of the enterprise based on the case of a

university business school.

Visual knowledge maps are a powerful tool for

enhancing understanding and fostering collaboration

in a company setting. These maps can be used to

https://orcid.org/0000-0002-3612-6014

https://orcid.org/0000-0003-1466-8100

https://orcid.org/0000-0002-2530-6765

visually represent information, ideas, and

relationships in a clear and concise manner, making it

easier for faculty and students to grasp complex

concepts and share knowledge with their colleagues.

Visualization allows to present complex data and

identify patterns, trends, and structures, which

facilitates deeper exploration of the data. Diagrams

allow all the employees and newcomers to expand less

cognitive energy deciphering the meaning of the text

they are reading, which means they will have more

cognitive energy available for the critically important

tasks of understanding, assessment and reflection

(Miller 2023, Moody 2007). The main benefits of

knowledge visualization are related to: stakeholder

engagement, flexibility, knowledge transfer, signalling

role, agility and interactivity (Troise, 2022). Using

knowledge representation and mapping help to

organize the smarter collaboration. The term was

coined by H. Gardner (Gardner, 2017) when she

described the need for highly-specialised experts to

come together in order to tackle more complicated

issues than any of them could do on their own.

130

Kuznetsova, A., Gavrilova, T. and Alkanova, O.

Building Atlas of Knowledge Maps: Towards Smarter Collaboration.

DOI: 10.5220/0013060200003838

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3: KMIS, pages 130-136

ISBN: 978-989-758-716-0; ISSN: 2184-3228

The paper discusses some preliminary results of

the METAKARTA project (MEthodology and

Technology for developing digital Knowledge mAps

for education and Research TeAms) where we

developed the methodology visualizing teaching and

research activity of the faculty members.

The paper structure is as follows: the current

section 1 provides the motivation for creating a new

approach, section 2 presents a brief literature review

and highlights the existing research gap, the atlas’

attributive ontology design is described in section 3,

while section 4 provides a demonstration of this

approach in a decision-making process.

2 KNOWLEDGE MAPPING

Knowledge maps are powerful information

visualization techniques that allow describing

knowledge assets, connecting experts, accessing

knowledge over time, existing knowledge resources

and knowledge gaps (Faisal et al., 2019). The main

tools that are widely used in knowledge mapping,

require the participation of both experts and analysts

who develop visual diagrams reflecting

▪ Sources of knowledge;

▪ Location of knowledge elements;

▪ Owners of knowledge elements;

▪ Links and relations between them, etc.

Knowledge maps are closely related to

competency maps and employee competency

management, which are denoted as skills and

competencies in corporate decisions (Anthony,

2021). Such maps turn enterprise data into valuable

and insightful information. Knowledge maps are one

of the tools used in knowledge engineering for

organizing and presenting knowledge, forming a

graphical framework and landscape in visualizing

complex concepts, decision support, knowledge

sharing, etc. (Balaid et al., 2016).

However little attention is paid to the

development of a well-structured set of visual

representations of key concepts, relationships,

knowledge owners of a knowledge domain of the

organization encouraging the employees to see the

big picture, promote collaboration, and improve

organization and focus.

The development of knowledge maps starts with

the definition of goals and stakeholders. For each

level, a basic atlas (visual set) of types of knowledge

maps was created.

2.1 Definition of Goals

In the field of management, the following goals may

be solved using the developed knowledge maps:

▪ optimization and activation of resources,

including the formation of project teams or

working groups taking into account the

principle of complementarity, ensuring the

transfer of knowledge from experts to

employees who have gaps (Liebowitz, 2005)

(in this case, an employee development plan is

formed based on such tools as coaching and

mentoring) and strategic planning for the

development of assets (Zack, McKeen, Singh,

2009) (based on the analysis of the map for

various areas of knowledge, a decision is made

to close gaps or change the focus of activity);

▪ identification of the hidden potential of

employees. The principle of completeness,

implemented in the construction of ontologies

of subject areas, provides a comprehensive

analysis and allows for the formalization of

those areas of knowledge that were previously

not in the field of view when assessing

employees. By discovering previously

unknown competencies and publications of

subordinates, a manager can make a more

informed (and therefore less risky) decision

about developing new areas of activity (Butt et

al. 2021).

2.2 A Stakeholder Analysis

Before the knowledge mapping study, a stakeholder

analysis was conducted. Stakeholders who influence

academic and research teams and benefit in one way

or another from access to the knowledge map data

may include both external and internal users and can

be divided into three categories: managers

(administrators), experts, and ordinary employees,

including newcomers (Pereira et al., 2023). The

METAKARTA project expanded the traditional

classification and identified another category:

external experts. In modern universities, the roles

described above are represented by internal

stakeholders: administration (managers), research

and teaching staff, including young scientists and

postgraduates (experts and ordinary employees).

Based on the fundamental differences between these

three groups of knowledge map recipients, a

classification was proposed at three levels: general,

focused, and detailed, as described in previous

publications. These three levels in the described case

correspond to:

Building Atlas of Knowledge Maps: Towards Smarter Collaboration

131

• institution level,

• department level,

• individual level.

The next two figures illustrate school and

department levels for shaping the research activity my

mapping the bibliometric data extracted from Google

Scholar.

Figure 1: Distribution of all the publications among the

school departments.

Figure 2: Portrait of department X.

Figure 1 shows the general level distribution of all

the publications listed in the database among the

university school departments. Here the information

on the percentage of the total amount of publications

of the university school departments is stated. That

helps to evaluate the more and less active departments

in terms of publications.

Figure 2 shows focused and detailed levels of

generalization describing a portrait of the faculty

from department X and gives the information on the

number of publications for each of the faculty

members, their H-index, the number of citations of

each of the teacher.

3 DATA COLLECTION

METHODOLOGY

The METAKARTA Project results include visual

representations of two information landscapes –

teaching and research. The essential part of the

project was devoted to data collection.

Bibliometric data was acquired from Google

Scholar, while teaching information needs two data

processing stages. Two surveys with self-assessment

questions were conducted where the faculty assess

their teaching competences and expertise. Initially a

set of secondary data was used from 2019-2020 larger

project initiated as part of the internal self-assessment

of the targeted school full-time faculty.

Organization of the data was as follows: each

respondent answered a series of binary questions

whether they consider themselves as an expert in a

particular area of knowledge from the predefined set

of ontologies. In case of a positive answer for a

particular area a set of questions regarding teaching,

research and applied consulting experience followed.

Consequently, the dataset was organized in a

“matrix” logic – the assessment of experience in each

type of activity was carried out for each area of

knowledge noted by the employee.

The analysis of results of the first survey helps to

prepare the second updated one.

Data for the new questionnaire were collected in

the middle of the 2023/2024 academic year from the

current full-time faculty of the same school as for the

first data collection (all full-time faculty members

who teach at least 1 course per year were surveyed).

Total sample size was 56 qualified faculty members.

The retention rate of the full-time staff between the

two datasets was 68.3%, which, provided that the data

from the first and second surveys are brought to a

single coding, makes it possible to build not only

maps reflecting the development of employees, but

also maps of changes by departments – a new set of

maps that show the dynamics of the internal

knowledge.

As a result of the two datasets comparison, we

found out that:

Time spent for questionnaire fulfillment

decreased by around 30-50% (depending on the

number of areas of expertise – the effect was higher

for employees with more areas of expertise.

The average number of knowledge areas reported

by employees as areas of expertise increased from 2.5

to 3.5, thus providing a more detailed picture of

knowledge in specific scientific areas (assessed across

employees who participated in two data collections). A

random check for deviations showed that, overall,

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additional areas of expertise were supported by

objective experience existing already by the time of the

first data collection, which proves increased data

accuracy when a sequential approach is used.

4 AN ATLAS’ ATTRIBUTIVE

ONTOLOGY DESIGN

Based on the extensive experience in working with

questionnaires in social sciences (Aithal & Aithal,

2020) a combination of primary and secondary data

was used to the maximum extent possible while the

development of knowledge maps for the atlas. It was

found that when building knowledge maps based on

primary information, there is a risk of obtaining

unreliable data. In terms of completeness of

information, the optimal solution is to combine

primary and secondary data to build knowledge maps.

In this case, different implementation scenarios are

possible:

▪ from secondary data to primary (building an

employee profile based on secondary data, then

verifying this profile by the employee as part of

collecting primary data with the ability to

additionally collect self-assessment data);

▪ from primary data to secondary (primary data

are verified based on available secondary data

- the expertise declared by the employee is

confirmed based on available objective

information);

▪ independent assessment of the employee

profile - building individual maps based on

subjective assessment and secondary data with

subsequent generalization on the expert profile

dashboard.

Atlas systematizes the significant properties of

knowledge maps bringing the connections among

them. We borrowed the term from classical

definition: Atlas is “a bound collection of maps often

including illustrations, informative tables, or textual

matter” (Merriam-Webster).

When creating an atlas of knowledge maps that

describes modern diagram templates and

recommendations for their use, work was carried out

to generalize and structurally describe the existing

diagrams. Information design in knowledge maps

aims to avoid confusion by presenting data in a way

that’s easy to understand. Based on the study of

researchers Lenger and Eppler who compiled a table

similar to the periodic table, consisting of more than

100 different visualization techniques, divided by

type of use (Lengler, Eppler, 2007) we include more

than 20 visual diagrams into the atlas. Also atlas

systematizes the recommendations for their use, it

describes modern diagram templates and structurally

summarize describe the existing diagrams in a form

of a table.

The conceptual representation of atlas may be

defined as an attributive ontology (Fig.3).

Figure 3: Structure of attriBUTive OnTology of kNowledge

maps: upper level.

The BUTTON Ontology (attriBUTive OnTology

of kNowledge maps) is a generalization and

systematic description of various characteristics

(attributes) of knowledge maps used to visualize the

information landscape of companies and universities.

This ontology summarizes many characteristics of

knowledge maps into three categories:

▪ content;

▪ format; and

▪ purpose of map.

The format of this conference paper does not

allow to show all the BUTTON ontology framework.

Using of the developed atlas create an additional

advantage for all its users and the project

stakeholders.

5 KNOWLEDGE ATLAS

The atlas of the knowledge maps presents systemic

vision of possible diagrams that scaffolds the

understanding of university intellectual assets from a

range of perspectives. The paper tries to provide

comprehensive insight into the ways in which

university and faculty members visualize their

bibliometric and teaching intellectual assets.

5.1 Classification Based on Content of

Knowledge Maps

The content of knowledge maps plays a key role in

determining their effectiveness and applicability in

Building Atlas of Knowledge Maps: Towards Smarter Collaboration

133

different contexts. It includes two main aspects: the

carrier and the elements of knowledge.

In the context of an educational institution, a

carrier (teacher) is an entity responsible for the

accumulation, transfer and acquisition of knowledge.

The main feature of the attribute "carrier" is its

"potential". The potential of the carrier (teacher)

reflects his or her cumulative knowledge, skills and

experience in certain areas and includes the depth and

breadth of the teacher's expertise. The dynamics of

the carrier reflect the changes in his potential over

time: the teacher's self-improvement, his participation

in professional training and education, as well as the

continuous updating of knowledge. Relationships

between carriers are a network of interactions,

exchange of knowledge and experience. This includes

various forms of cooperation, such as the exchange of

educational materials, joint research projects, etc.

In the context of an educational institution, a

carrier (teacher) is an entity responsible for the

accumulation, transfer and acquisition of knowledge.

The main feature of the attribute "carrier" is its

"potential". The potential of the carrier (teacher)

reflects his or her cumulative knowledge, skills and

experience in certain areas and includes the depth and

breadth of the teacher's expertise.

The knowledge elements on the map include

specific learning materials and information elements

belonging to the carriers. Elements can be organized

into different structures, have priorities, locations,

and formats. By the structure of knowledge elements,

we understand ways of classifying, organizing and

linking individual elements of knowledge to ensure

their accessibility and understanding. The structure

helps to navigate in the set of knowledge, understand

their interrelations and find the necessary

information. Knowledge elements are prioritized

subjectively by managers and reflect their

understanding of the importance and relevance of

knowledge components in the context of a particular

area or task. Prioritization allows you to identify

aspects that should be paid attention to when planning

training programs, courses and human resources. The

location of knowledge elements includes the

geographical location of the teacher (for example, in

a branch of the university), the academic unit

(department, faculty) and the program.

5.2 Classification Based on Format of

Knowledge Maps

The shape of knowledge maps is an important aspect

of their visual representation, determining the way

information is displayed. For knowledge maps, we

have considered graphs, tables, charts, as well as

metaphor drawings as shown in Figure 4.

Figure 4: Possible Formats of knowledge maps in the atlas

The atlas is designed in the form of the table with

a description of the difficulty level of the diagram, the

preview of the pictogram, its design and the main

characteristics and purpose. The maps include four

major types as shown in Figure 4:

▪ Tables;

▪ Graphs;

▪ Charts;

▪ Metaphors.

Tables include one-level, multilevel and nested

tables as shown in Figure 5.

Figure 5: Types of tables in the atlas.

Heat map tables serve graphical representation of

data using color and size to encode text tables for

easier comparison of data values.

Figure 6: A heat map table: overview of one department’s

knowledge.

The heat map table in Figure 6 presents an

overview of the level of expertise of professors in one

of the departments of the university based business

Tables

One-level

Multilevel

Nested

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school. The collected data in the table presents the

coded names of professors (columns) and the

corresponding fields of knowledge (rows).

The capacity of the level of expertise of each

professor is determined by the sum of spheres of

competence within multiple fields of knowledge,

which are presented in the corresponding cells.

The resulting table provides an efficient and easy-

to-read presentation of the level of expertise of each

professor according to their self-assessment.

The table can be used to identify areas of strengths

and weaknesses among professors, and to allocate

resources more effectively based on each professor’s

expertise. Further research could explore the

relationship between the level of expertise of

professors and the quality of their teaching and

research outcomes.

There are two types of graphs as shown in Figure

▪ Hierarchical

▪ Network undirected.

▪

Figure 7: Types of graphs in the atlas.

Hierarchical tree is a graphical representation of

hierarchically organized data in the form of a tree.

Network maps include five types:

▪ Radar - a graphical representation of data in the

form of petals, typically used to compare

different categories or aspects.

▪ Radar with Markers - a radar chart where in

addition to the petals, markers are also

displayed to indicate values.

▪ Filled Radar – a radar chart in which the areas

between the petals are filled with color for

better visualization.

▪ Arc diagram is useful to reveal the overlap of

data.

▪ Chord diagram reveals the relationship

between the objects inside the organization.

Charts include one-, two- and three-dimensional

ones. Metaphors include images that are used as

universal metaphors to visually organize the

information (physical landscape, pyramid, fishbone,

etc.)

5.3 Classification Based on Purpose

The characteristics of a purpose include

▪ purpose itself,

▪ focus,

▪ stakeholders,

▪ level of generalization.

The purpose of knowledge maps plays a key role

in their creation and can be considered in different

contexts, e.g. - decision-making / market positioning

/ raising general awareness within the company.

"Focus" refers to the main focus of using a

knowledge map. Within the framework of the study

of the experience of teachers in three areas, the

following types of focus can be distinguished:

academic work / research / consulting and projects.

Stakeholders in our study include external and

internal users. External ones include applicants,

business partners and customers who can use

knowledge maps to obtain information about the

educational institution, projects and employees.

Internal users are administration, teachers,

researchers and students.

The level of generalization of knowledge maps,

which is determined by the purpose and task of

mapping, is also important. It can be either universal

or specialized. Universal knowledge maps are

applicable in various fields of knowledge and

disciplines, showing the general picture of what is

happening (for example, a faculty science citation

map). Specialized knowledge maps can be focused on

specific areas or levels, such as faculty, graduate

school, department, or individual faculty.

6 CONCLUSION

The information space of organizations is overloaded,

there is a need to find convenient assistants that

facilitate the processing of information for users. The

most difficult and labour-intensive part of working

with information is associated with its search,

structuring, and compression. The visual approach is

one of the possible ways to scaffold the information

flow.

The paper discusses the developing of the

prototype of an atlas of knowledge maps describing

the intellectual assets of the university business

school. This prototype of the atlas includes invariant

Graphs

Hierarchical

Network

Radar

Radar with

Markers

Filled Radar

Arc

Diagram

Chord

Diagram

Building Atlas of Knowledge Maps: Towards Smarter Collaboration

135

representations of knowledge maps of educational,

scientific and consulting activities, depending on the

stakeholder, the task itself, the selected level of

generalization for mapping

(institute/department/individual).

One of the key benefits of using this atlas is that

the maps from it help students, faculty and

administration see the big picture of the academic life.

By mapping out key concepts and their

interconnections, employees can gain a better

understanding of how different pieces of information

fit together, who are the experts and how they

contribute to the overall goals of the university unit in

teaching and research. This can help employees make

more informed decisions and work more effectively

towards shared objectives and smarter collaboration.

Systematic analysis of corporate, administrative

and scientific knowledge creates the potential to

significantly improve the quality of information

support, creating knowledge management systems for

more effective interaction between various groups of

organization employees and external users and

stakeholders.

Ultimately, using visual knowledge maps can lead

to smarter decision-making, more innovative

solutions, and a more efficient and effective

company. It is a step to visual organization (Bell,

Warren, & Schroeder, 2014).

ACKNOWLEDGEMENTS

Authors thank Dr. Dmitry Kudryavtsev, who was the

initiator of the project and started the research design,

and Miroslav Kubelsky for the digital support of

bibliometric maps. The work was carried out by

Gavrilova T.A., Alkanova O.N. as part of

METAKARTA PROJECT grant No. 23-21-00168,

https://rscf.ru/project/23-21-00168/.

REFERENCES

Aithal, A., Aithal, P. (2020). Development and Validation

of Survey Questionnaire & Experimental Data—A

Systematical Review-Based Statistical Approach.

International Journal of Management, Technology, and

Social Sciences (IJMTS), 5, 233-251.

Anthony, Jr, B. (2021). Information flow analysis of a

knowledge mapping-based system for university

alumni collaboration: a practical approach. Journal of

the Knowledge Economy, 12(2), 756-787.

Balaid, A., Abd Rozan, M.Z., Hikmi, S.N., and Memon, J.

(2016). Knowledge Maps: A Systematic Literature

Review and Directions for Future Research,

International Journal of Information Management, Vol

36, No. 3, 451–475.

Bell, E., Warren, S., and Schroeder, J. (2014). Introduction:

The visual organization. In The Routledge companion

to visual organization (pp. 1-16). Routledge.

Butt, S. A. (2021). et al. A knowledge map for ICT

integration in the silver economy. Procedia Computer

Science, (181), 93-701.

Faisal, H., Rahman, A., and Zaman, G. (2019). Knowledge

Mapping for Research Papers. International Journal of

Computer Science and Network Security, 19 (10), 158–

164.

Gardner, H. (2017). Smart Collaboration: How

Professionals and Their Firms Succeed by Breaking

Down Silos. Harvard Business Review Press.

Lengler, R., Eppler, M.J. (2007). A Periodic Table of

Visualization Methods. URL: https://www.visual-

literacy.org/periodic_table/periodic_table.html

Lengler, R., Eppler, M.J. (2007). Towards a periodic table

of visualization methods of management.

Liebowitz, J. (2005). Linking social network analysis with

the analytic hierarchy process for knowledge mapping

in organizations. J.of knowledge management,9,76–86.

Merriam-webster dictionary (https://www.merriam-

webster.com/dictionary/atlas#:)

Miller, F. The power of Diagrams.

https://www.francismiller.com/articles/

Moody, D. (2007). What makes a good diagram? improving

the cognitive effectiveness of diagrams in is

development. In Advances in Inform. Systems

Development: New Methods for the Networked

Society. Boston, MA: Springer US. 481-492.

Pereira, V., Bamel, U., Temouri, Y., Budhwar, P., Del

Giudice, M. (2023). Mapping the evolution, current

state of affairs and future research direction of

managing cross-border knowledge for innovation.

Intern. Business Review. – Vol. 32, No. 2, 101834.

Troise, C. (2022). Exploring knowledge visualization in the

digital age: an analysis of benefits and risks.

Management Decision, Vol. 60 No. 4, pp. 1116-1131.

https://doi.org/10.1108/MD-01-2021-0086

Zack, M., McKeen, J., and Singh, S. (2009). Knowledge

management and organizational performance: An

exploratory analysis. Journal of Knowledge

Management, 13(6), 392–409. https://doi.org/10.1108/

13673270910997088

KMIS 2024 - 16th International Conference on Knowledge Management and Information Systems

136