Regional Knowledge Maps

Potentials and Challenges

Montserrat Garcia-Alsina

, Christian Wartena

and Sönke Lieberam-Schmidt

Information and Communication Sciences Department, Universitat Oberta de Catalunya,

Rambla del Poblenou, 156, 08018 Barcelona, Spain

Department of Media, Information and Design, University of Applied Sciences and Arts Hanover, Hanover, Germany

Keywords: Knowledge Maps, Regional Development, Text Mining, Regional Policy.

Abstract: Regional knowledge map is a tool recently demanded by some actors in an institutional level to help

regional policy and innovation in a territory. Besides, knowledge maps facilitate the interaction between the

actors of a territory and the collective learning. This paper reports the work in progress of a research project

which objective is to define a methodology to efficiently design territorial knowledge maps, by extracting

information of big volumes of data contained in diverse sources of information related to a region.

Knowledge maps facilitate management of the intellectual capital in organisations. This paper investigates

the value to apply this tool to a territorial region to manage the structures, infrastructures and the resources

to enable regional innovation and regional development. Their design involves the identification of

information sources that are required to find which knowledge is located in a territory, which actors are

involved in innovation, and which is the context to develop this innovation (structures, infrastructures,

resources and social capital). This paper summarizes the theoretical background and framework for the

design of a methodology for the construction of knowledge maps, and gives an overview of the main

challenges for the design of regional knowledge maps.

1 INTRODUCTION

In the present paper we investigate the value of

knowledge management if it is applied to geographic

regions. Especially we focus on the role of regional

knowledge maps for regional innovation

management. We argue that more research is needed

in this area in order to collect, structure and make

efficient use of the vast amount of mostly textual

and unstructured information that is available for

almost every region in industrial countries.

In the first place, knowledge management (KM)

provides a framework to identify which knowledge

is created or needed in the different organizational

processes, and how it can be created and stored to

generate value in an organization (Raghu and Vinze,

2007); (CEN, 2004). In this context, knowledge

maps play an important role.

Applied to the territories, knowledge

management identifies how the knowledge cycle

works in a territory, and it is considered a way to the

regional innovation, important in an economy based

on knowledge and innovation (Asheim and Coenen,

2005); (OECD, 1996); (Lundvall, 1992). In this

sense, recently, some authors have pointed out

knowledge maps as an instrument to manage

knowledge in a territory and to promote its

development (Barinani et al., 2013, España, 2011).

Secondly, KM as discipline for years has

developed research about the role and value of

knowledge maps, but offers little research about how

to do it, and how to create regional knowledge maps

(Watthananon and Mingkhwan, 2012); (Driessen et

al., 2007); (Huijsen et al. 2004); (Kim et al., 2003);

(Eppler, 2001); (Wexler, 2001). More concretely,

regional knowledge maps could collect the territorial

knowledge created by the different actors involved

in a region (authorities, clusters, companies,

universities, NGO, etc.). They could also collect the

information’s fluxes between these actors. Their

analysis contributes to identify the strengths and

weaknesses at national or regional level, to produce

new insight capabilities for regional stakeholders

(industry, academia, and civil society) regards

identifying new areas of applied research, how to

promote industrial leadership in a region, and how to

coordinate and integrate research agendas and

514

Garcia-Alsina M., Wartena C. and Lieberam-Schmidt S..

Regional Knowledge Maps - Potentials and Challenges.

DOI: 10.5220/0004627105140519

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge

Management and Information Sharing (KMIS-2013), pages 514-519

ISBN: 978-989-8565-75-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

actions. Consequently, they could help in a local

way the decision-making needed for a consistent

implementation of regional policy coordination in

order to optimize ressources.

Hence, building territorial knowledge maps to

support innovation and regional development

represents a major step forward, due to the

complexity of aspects that these maps should collect:

which processes take place in a territory, which

knowledge is created or needed in the different

processes, how and where can knowledge be created

and stored, which structures and infrastructures exist

in a region related to knowledge and innovation, and

the information fluxes among its actors.

This paper reports on work in progress on the

design of a methodology to develop territorial

knowledge maps as a tool for regional development.

The remainder of this paper is structured in four

parts. Firstly, we specify the objectives and the

research questions. Secondly, we expose shortly the

theoretical backgrounds used as basis to develop the

theoretical framework. Thirdly, we describe the

challenges faced to build knowledge maps. Finally,

we present the conclusions.

2 OBJECTIVES AND RESEARCH

QUESTIONS

Our objective is defining a methodology to

efficiently design territorial knowledge maps, by

extracting information of big volumes of data

contained in diverse sources of information related

to the territory. The methodology, of course, should

be independent of the specific.

Consequently, the work implies facing several

challenges, which we have concretised in the

following objective: knowing how we can extract

knowledge from a wide amount of sources related to

one region and visualize it to get a complete picture

of the region’s innovation potential. To

conceptualize how to do so is the case for the project

in process presented in this paper which turn on the

following research questions:

1. Which information sources help to find this

knowledge?

2. Which are the territory’s actors involved in

innovation?

3. How can we identify the knowledge available in

a certain region?

4. How can we study the social relationships,

conventions, norms and rules that influence

innovation?

5. How can we mine the territorial data to create

knowledge maps that enable stakeholders to

apply them for innovation and regional

development?

6. How can we identify innovation potentials that

remain hidden and unobserved until now?

7. How can we identify obstacles hindering

innovation?

The first four questions are directly related to the

project’s objective, because they point out to which

methodology is adequate to build knowledge maps.

The last two are oriented to detect which knowledge

is important to be incorporated in a regional

knowledge map to assure the focus on innovation.

3 THEORETICAL

BACKGROUND

Taking into account the aims mentioned above, our

research is based on four perspectives, each of which

has its own corpus of knowledge. These perspectives

are: knowledge management, competitive and

territorial intelligence, Regional Innovation Systems,

and text mining.

Knowledge Management (KM) is a methodology

that integrates the activities embedded in the

organizational processes to obtain organisational

aims and manage intellectual capital (Raghu and

Vinze, 2007; Ergazakis et al., 2005; CEN, 2004;

Bolinger and Smith, 2001; Beijerse, 1999). KM

applied to private or public organizations and to the

territory contributes to generate competitive

advantages (Danskin et al., 2005).

Competitive Intelligence (CI) until the present

has developed a framework to manage strategic

information oriented to generate intelligence in the

organisations scale, but in the territory scale studies

are incipient and are named Territorial intelligence

(TI). This framework is based on a cycle, which

considers the following phases: identification of

information’s needs, its acquisition, its organisation,

its analysis, and knowledge/intelligence creation

(CAENTI, 2012); (Garcia-Alsina and Ortoll, 2012).

Text mining and analytics offers the tools to

exploit the wide quantity of unstructured data to

extract information, which once analysed becomes

information and knowledge. Text mining enables the

automatic analysis of large amounts of text from the

internet (companies, institutes, governments, etc.)

(Kosala and Blockeel, 2000); (Moens (ed.), 2006).

Using techniques like named entity recognition

(Nadeau and Sekine, 2007) and topic detection

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515

(Wartena and Brussee, 2008) it is possible to find

the main (innovative) products an industry in a

region is working on, and not only its existing

relations, but the missing relations, too. Text mining

is used for business intelligence, for KM within

companies and for studying customer behaviour and

satisfaction (Saggion et al., 2007), and to monitor

new developments in a certain field of technology

(Färber and Rettinger, 2013). Text mining is also

used to find experts and networks of experts (Ehrlich

et al., 2007). The application of text mining to

regional intelligence to systematically mine the

expertise and innovative potentials of all companies

in a region is new and will have to deal with a host

of challenges concerning the interaction of crawling

and analysing web content

Finally, National or Regional Innovation Systems

(NIS or RIS) offer the framework to identify which

kind of actors, resources, social capital, structures,

institutions and organizations are involved in the

diffusion of new technologies and infrastructures in

a geographical area to support innovation and

regional development (Andersson, 2013); (Chen and

Guan, 2011); (Jimenez et al., 2011); (Sharif, 2006);

(Doloreux and Parto, 2005); (Asheim and Coenen,

2005); (Cooke, 2001); (Cooke et al., 1997);

(Edquist, 1997).

More concretely, our theoretical framework deals

with the following topics, which can lead or promote

innovation and development in a region (Fig. 1).

Figure 1: Parts of a regional innovation system.

Firstly, we must to identify which are the

regional actors in a region, who has been identified

in previous studies: higher institutions, public and

private sectors and social spheres (Jimenez et al.,

2011); (Fröhlich, 2010); (Sharif, 2006); (Voß et al.,

2002). The second element to study is the structural

elements related to economy (kind of sectors or

firms’ size), knowledge (disciplines, institutions)

Politic and Administration (parties, parliament, local

government, and associations) (Fröhlich, 2010);

(Voß et al., 2002). Thirdly we focus on

infrastructures: technic (water, wastewater, energy

and transport), social (health, education, culture),

finance (banking sector, venture capital and

government aid), policies, and science and

innovation systems (Voß et al., 2002); (Edquist,

1997). The next element to be considered is the

resources existing in a territory, those related to the

Nature (geology, water, air, climate, accumulations

capacity), row material (minerals, vegetarian,

animal), demography (inhabitants, structure),

regional innovative capabilities and intangible assets

(intellectual capital) (Doloreux and Parto, 2005);

(Voß et al., 2002) Lastly, as innovation is a social

process through which organizations learn

(Doloreux and Parto, 2005), we incorporate the

framework of social capital as an element to be

considered in the knowledge map. Social capital

refers to which social relationships and in which

context take place to generate knowledge as basis to

innovation. More concretely, this framework gives

insights about which are the institutional

collaboration (universities – government – industry)

(Etzkowitz and Leydedorff, 1995), and the different

dimensions that could influence the social capital:

structural dimension (networks ties, network

configuration, and appropriable organization);

cognitive dimension (shared codes and languages,

shared narratives); and relational dimensions (trust,

norms, obligations identification) (Nahapiet and

Ghoshal, 1998).

4 CHALLENGES

When trying to build a regional knowledge map we

face a number of difficulties. The most important

problems are 1) the lack of clear definition of

regional knowledge maps and a methodology for

their development, and 2) the wide variety of topics

to be considered, which require identifying the

adequate information sources, dealing with different

kinds of formats and structures, and different

methods to design the data collection, sampling, and

data analysis. Other challenges are consequence of

these central problems. We find challenges in the

following areas: a) methodology to build knowledge

maps, b) identifying elements from a RIS (actors,

structures, infrastructures and resources) and their

information sources, c) discovering social capital

and social networks in the region between actors,

and d) analysis process.

4.1 Methodology

The few works dedicated to knowledge maps have

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developed techniques oriented to: a) capture explicit

and tacit knowledge, b) analyse knowledge areas in

organizations, c) identify through which

organization’s information sources, the

organizational knowledge can be captured, d) and to

illustrate how knowledge flows throughout an

organization (Kim et al., 2003). Nevertheless, the

typology of the sources to consider, and the actors

and processes developed in an organization are

different in this works from those studied in regional

studies. Thus, new ways to develop knowledge maps

must be explored. Besides, the methods used to

collect and extract information to draw knowledge

maps often are qualitative.

4.2 Identifying Elements from a RIS

Identifying the elements included in a RIS and their

sources is another challenge to this project. On the

one hand side, the vast amount of actors in a

reasonable region that can easily exceed the number

of 100,000, and the diversity of the information

sources, make it to a real challenge to achieve

completeness and an equable granularity of

information. Especially those actors that are either

very small, very new or not well connected in

networks and/or the Web will be difficult to identify.

The same happens with structures and institutions,

because they are different between regions, and also

they could lack presence on Internet. Moreover,

there are hardly any lists available of all actors in a

region. Since websites of companies not necessarily

link to other companies in the same region, crawling

the web by following links is not an efficient option.

Thus information hubs for a region have to be found

that link to, or mention the most important actors in

a region. These information hubs might serve as a

starting point for crawling information. Potential

information hubs include chambers of commerce,

business associations, business networks etc. Also

printed lists from such organizations and from local

governments constitute useful sources of

information.

More fundamentally, we have to define what the

actors in a region are. Companies and organizations

are structured across borders of regions. Companies

might be present in a region but only with a small

part of their activities. It can be very hard to identify

the role a company plays in a region. Public

available sources of information are not designed to

reveal these structures, but are usually consumer

oriented and give only addresses of headquarters and

sales offices. Finally, each region has a lot of actors

that might be uninteresting for a knowledge map for

regional innovation. Each village will have a bakery,

a plumber etc. that are uninteresting for the final

picture of the innovative potentials of a region.

Nevertheless, also a bakery or a plumber can be an

innovative company and advance to an important

regional or supra regional actor. Thus the decision,

which actors to include in a regional knowledge map

and which not, is a further challenge.

The identification of social capital and social

networks between actors and their influence in

innovation process is another challenge. However,

this discipline in the last years has advanced and

methodologies and software tools for social network

analysis have become available.

4.3 Analysis

Finally, during the analysing process the text and

link mining procedures (Lieberam-Schmidt, 2010)

have as challenge to find the right tradeoff between

manually performed work and machine based

automatisms. While manual work may achieve

higher quality of results, only automated text mining

methods will be able to process the vast amount of

information sources. Using information crawled

form websites of companies, preliminary results

show that it is feasible to find addresses, phone

numbers, etc. by named entity detection on the

crawled texts. For this purpose regular expressions

for these entities were defined and the number of

occurrences of entities on the web pages is counted.

The most frequently found address and phone

number usually indeed present the company’s main

contact information. Also, we have very

encouraging results with respect to the classification

of the main activity of a company using their Web

presence. For this purpose we used the main

economic sectors of the STW Thesaurus for

Economics (Gastmeyer 1998); (Neubert 2009).

On the other hand it turns out to be much more

challenging to find relations between companies or

to find products or services made by a company. The

main problem is the diversity of the resources and of

the type of actors. Again a clear definition of actors

and a missing correspondence between actors and

information resources poses a major problem. Large

companies and institutions, like universities, cannot

be seen as single actors that have one main activity.

Rather we have a complex and partly obscure

hierarchy of actors for which it is already difficult to

identify at which level in the hierarchy the activities

should be classified.

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517

5 CONCLUSIONS

The design of regional knowledge maps emerges as

an important topic of research because their content

could enable the strategic planning and decision-

making process in regional policy. Regional

knowledge maps could also enable the relations

between actors and the collective learning as a basis

to the innovation.

Their design requires capturing all the

information about the regional competencies

composed by infrastructures, structures, resources,

actors, knowledge and social capital. The collection

and the representation of these elements present a

number of challenges that must be resolved.

The challenges could be faced with the

conjunction of different disciplines to define a

methodology. The theoretical corpus of knowledge

management as discipline offers a basis to develop

this methodology, so in the future this topic could be

one more to be considered with the alliance of other

areas of research: text mining, regional innovation

systems and competitive and territorial intelligence.

This paper has the will to help starting this new

line of research. The challenges presented are the

result of the first phase of an exploratory research

project. On the one hand the project focuses on the

identification of information sources related to a

region, from which we extract information as a first

approach to the objective. On the other hand we

identify which tools facilitate extracting information

of these sources, according to the theoretical

framework designed. During this phase we delimit

the search to some specific elements of the RIS in a

small geographical area, concretely the region of

Hanover (Germany). Finally, we will validate this

methodology applying that to other regions.

ACKNOWLEDGEMENTS

This research is funded by the Spanish Ministry of

Education, Culture and Sport (Ref. CAS 12/00155).

The Catalan Government’s Commissioner for

Universities and Research supports the KIMO

research group on knowledge and information

management in organisations.

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