Supporting Competitive Intelligence with Linked Enterprise Data

Vitor Afonso Pinto

, Guilherme Sousa Bastos

, Fabricio Ziviani

and Fernando Silva Parreiras

LAIS – Laboratory of Advanced Information Systems, FUMEC University, Belo Horizonte, MG, Brazil

IESTI – Institute of Systems Engineering and Information Technology, UNIFEI, Itajuba, MG, Brazil

Keywords:

Semantic Web, Linked Data, Linked Enterprise Data, Competitive Intelligence.

Abstract:

Competitive Intelligence is a process which involves retrieving, analyzing and packaging information to offer

a ﬁnal product that responds to the intelligence needs of a particular decision maker or community of deci-

sion makers. Internet-based information sources are becoming increasingly important in this process because

most of the contents available on the Web are available free of charge. In this work the following research

question was addressed: What are the concepts and technologies related to linked data which allow gathering,

integration and sharing of information to support competitive intelligence? To answer this question, ﬁrstly,

the literature was reviewed in order to outline the conceptual framework. Next, some competency questions

were deﬁned through a focus group in a study object. Finally, DB4Trading tool was built as a prototype able

to validate the conceptual framework. Results point out that adoption of Semantic Web technologies enable to

obtain the data needed for the analysis of external environments. Besides that, results indicate that companies

use Semantic Web technologies to support its operations despite consider these technologies as complex. This

work adds to the decision-making process, specially in the context of competitive intelligence. This work also

contributes to reducing costs to obtain information beyond organization boundaries by using Semantic Web

technologies.

1 INTRODUCTION

Organizations today seek to thrive in turbulent times.

As environments become increasingly volatile, orga-

nizations are turning their gaze to the external hori-

zon, watching and grappling with a confusion of sig-

nals, messages, and cues. Sensing and making sense

of the environment have become the sine qua non for

organizational growth and survival. (Bouthillier and

Shearer, 2003). However, a great deal of data is avail-

able through direct and open channels. These chan-

nels include: 1) Published sources; 2) Governmental

data; 3) Other public documents. (Gelb et al., 1991).

Semantic Web works to create an environment

where people work together sharing knowledge and

having tools for information management and anal-

ysis. (PAN, 2009). Linked Data initiative was pro-

posed to remove the barriers to data access and shar-

ing and also to enable data from different sources to

be connected and queried. (Hu and Svensson, 2010).

All these characteristics, if used in a corporate envi-

ronment, could facilitate competitive intelligence.

1.1 Competitive Intelligence

In order to obtain the sufﬁcient knowledge to under-

stand both the internal and external environment as a

whole, organizations perform coordinated actions to

seek, treat, distribute and protect information. (Tara-

panoff, 2006). The set of activities performed by or-

ganizations to gather information about competitors,

products and markets, is called Competitive Intelli-

gence (CI). (Moresi, 2006). It is also seen as seeking

any information which improves the organization po-

sitioning. (Tarapanoff, 2006). Intelligence gathering

goes on everyday, without necessarily being called by

its rightful name. More speciﬁcally, it implies legal

research efforts by business studying their competi-

tor’s products, organizations and related matters. It

is deﬁned as the use of public sources to develop in-

formation on competition, competitors, and the mar-

ket environment, including economic, regulatory, po-

litical, demographic inﬂuences, etc. (Cronin et al.,

1994).

The importance of such external data will not be

fully demonstrated if they are not combined with in-

ternal enterprise data and consumed in realtime busi-

409

Afonso Pinto V., Sousa Bastos G., Ziviani F. and Silva Parreiras F..

Supporting Competitive Intelligence with Linked Enterprise Data.

DOI: 10.5220/0005472604090415

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 409-415

ISBN: 978-989-758-096-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

ness decision making. (Hu and Svensson, 2010).

But, specially in large organizations, enterprise data

is available through a highly complex enterprise-wide

IT system, as several hundreds of interconnected sys-

tems may be employed. (Lindstr

om et al., 2006). And

the size of each single system may vary extensively

from enterprise resource planning systems to custom-

made niche products, making the system interconnec-

tions numerous and heterogeneous.

This way, in order to perform Competitive Intelli-

gence, companies need to integrate data from internal

systems (which are not always integrated) and also

data generated outside its boundaries (which are not

always structured or standardized). In this context,

Semantic Web is presented as a solution for increase

Competitive Intelligence through interlinking hetero-

geneous data sources.

1.2 Semantic Web

Semantic Web is a vision: the idea of having data

on the web deﬁned and linked in a way that it can

be used by machines - not just for display purposes,

but for using it in various applications. (Xu et al.,

2004). It is a web that includes documents, or por-

tions of documents, describing explicit relationships

between things and containing semantic information

intended for automated processing by machines. (Li,

2002). It also carries the promise to make web

machine-understandable by enriching available infor-

mation with logic-based semantics and provide us

with a new paradigm for knowledge interchange and

sharing. (Chen and Wu, 2003).

The fundamental concept of the Semantic Web is

to make the information, available on the web, more

meaningful by making it accessible to automated

tools that can augment our experience. (Niles and

Jeremijenko, 2001). It provides a common framework

that allows data to be shared and reused across appli-

cation, enterprise, and community boundaries. (Chen

et al., 2012). It also refers to a range of standards,

modeling languages and tool development initiatives

aimed at annotating Web pages with well-deﬁned

metadata, so that the semantics associated with web

contexts can be effectively interpreted. (Abrahams

et al., 2004).

1.2.1 Linked Data

Linked Data is a term describing a set of best prac-

tices to facilitate the publishing, accessing and inter-

linking of the data of the Semantic Web. Thus, Linked

Data is an open framework for the loose integration

of data in the Internet, where data sources can eas-

ily cross-link. (Feridun and Tanner, 2010). It is the

data exposed, shared, and connected via URIs on the

Web. It uses URLs to identify things as resources to

facilitate people to dereference them. It also provides

useful information about these resources, as well as

links to other related resources which may improve

information discovery. (Mi et al., 2009).

A pragmatic vision of the Semantic Web has

emerged via the Linking Open Data project (LOD),

focusing on translating various datasets available on

the Web into RDF and interlinking them, following

the Linked Data principles. Lots of different datasets

have been provided via this LOD initiative, such as

DBpedia (the RDF export of Wikipedia) or Geon-

ames (a large geolocation database). All together,

they form a complete Webscaled graph of interlinked

knowledge, commonly known as the Linked Open

Data Cloud.

1.2.2 Linked Enterprise Data

Another goal of Semantic Web is to apply its tech-

nologies for Enterprise Information Integration, cre-

ating a Corporate Semantic Web. (Eisenberg and

Kanza, 2011). Today’s business is based on huge

amount of information and extracting right informa-

tion at right time is a difﬁcult and tedious task. By

applying semantics within structured (ERP, Billing,

Financial, HR systems) and unstructured data (email,

fax, ofﬁce documents) we can take business decision

on the basis of overall organization knowledge base.

(Khan and Hussain, 2009).

Linked Enterprise Data (also called as ”Linked

Data Enterprise”) is an organization in which the act

of information creation is intimately coupled with the

act of information sharing. In a linked data enter-

prise, individuals and groups continue to produce and

consume information in ways that are speciﬁc to their

own business needs, but they produce it in a way that

can be connected to other aspects of the enterprise.

(Allemang, 2010).

This study intends to answer the following re-

search question: What are the concepts and tech-

nologies related to linked data which allow gather-

ing, integration and sharing of information to support

competitive intelligence? The main objective of this

study is to analyze the application of linked data tech-

nologies into gathering and integrating data generated

both inside and outside an organization for supporting

the competitive intelligence process, in the context of

portfolio management.

To achieve this goal, initially a systematic liter-

ature review was performed aiming to propose and

outline a conceptual framework to support this study.

Next, a focus group was conducted intending to iden-

tify competency questions, that is, variables and data

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

410

sources that could be used to build and validate the

proposed framework. Next, a prototype for integrated

data visualization was built, based on proposed frame-

work. Finally, the conceptual framework was tested

and validated, using the competency questions.

2 CONTRIBUTION

In order to address its objectives, our research was

divided in three major phases:

• Phase 1 - Outline the conceptual framework

through a Systematic Literature Review

• Phase 2 - Identify competency questions through

a Focus Group survey

• Phase 3 - Build, test and validate a prototype for

integrated data visualization

In this section, methods used to produce the re-

sults of this study are described.

2.1 Phase 1 - Systematic Literature

Review

In order to understand the applications of linked data

for corporate environments, we performed a compre-

hensive literature review on the state-of-the-art in the

research ﬁeld of Linked Enterprise Data. A System-

atic Literature Review (SLR) is a means of evaluating

and interpreting available research relevant to a par-

ticular research question, topic area, or phenomenon

of interest. (Kitchenham, 2004). This kind of study

comprises three consecutive steps: planning, execu-

tion and results.

The main purpose of the planning step is to de-

liver a protocol which drives the literature review ef-

forts. After analyzing the existing literature related

to Semantic Web and its application on corporate en-

vironments, the research questions addressed by the

systematic literature review were deﬁned. Next, the

strategy used to search for primary studies, includ-

ing search terms and resources to be searched was de-

ﬁned. Finally, the exclusion criteria and the strategy

to extract data from selected papers were deﬁned.

In the execution step, activities to extract and syn-

thesize data from papers were performed. First, the

primary studies were selected according to the deﬁned

data extraction strategy and exclusion criteria. After

selecting primary studies, the information needed to

address the questions of this review were collected,

that is, primary studies were examined. Selected pa-

pers were analysed considering the information re-

quired by each research question. Finally, the re-

sults of the included primary studies were collated

and summarized.

2.2 Phase 2 - Competency Questions

In order to identify competency questions, that is,

variables and data sources that could be used to build

and validate the proposed framework, we applied a

focus group methodology, which is understood as a

way of collecting qualitative data, and involves en-

gaging a small number of people in informal group

discussions, ’focused’ around a particular topic or set

of issues. (Hyland et al., 2014).

In the design step, time and location were deﬁned.

Also, participants were selected based on their expe-

rience with competitive intelligence. Basically, three

types of professionals were selected: strategy special-

ists, engineers and health, safety & sustainability pro-

fessionals. We also generated the focus group script

and other forms to support the overall process. Fi-

nally, the questions used in the focus group were care-

fully chosen aiming at achieving the general purpose

of focus group.

In the execution step, participants were informed

of the study and received a written information

form, including information on the aim of the study,

anonymity issues, and a ﬁeld for signing informed

consent. A topic guide was then followed, in order to

evoke the research themes during the discussion. The

focus group session was audiotaped and a member of

the research team took detailed notes of the discus-

sions.

Finally, the notes taken during the focus group dis-

cussions were used for the analysis of the collected

data, generating the results. Whenever clariﬁcations

of these notes were needed, the audiotapes were con-

sulted.

Participants mentioned the existence of speciﬁc

and generic datasources. ’Speciﬁc Datasources’ may

vary depending on the country, business or competitor

being analyzed. ’Generic Datasources’ can be used in

multiple analysis, independently of country, business

or competitor. Participants also separated datasources

applicable to multiple segments from those applica-

ble uniquely to their segment. Table 1 presents a list

of the identiﬁed datasets.

2.3 Phase 3 - Prototype for Integrated

Data Visualization

In order to test if semantic web technologies could

be used to support competitive intelligence a proto-

type for integrated data visualization was built. The

prototype was implemented following the framework

SupportingCompetitiveIntelligencewithLinkedEnterpriseData

411

Table 1: Generic Datasources.

DATASOURCE WEBSITE ADDRESS

GOOGLE https://www.google.com/

DOING BUSINESS PROJECT http://portugues.doingbusiness.org/

UNITED NATIONS http://www.un.org/

WORLD HEALTH ORGANIZATION http://www.who.int/en/

WORLD TRADE ORGANISATION http://www.wto.org/

CENTRAL INTELLIGENCE AGENCY https://www.cia.gov/index.html

DELLOITE http://www.deloitte.com/

LONDON METAL EXCHANGE http://www.lme.com/

INTERNATIONAL MONETARY FUND http://www.imf.org/external/index.htm

EDGARD https://www.sec.gov/edgar/aboutedgar.htm

SEDAR http://www.sedar.com/homepage en.htm

REUTERS http://br.reuters.com/

ABNT http://www.abnt.org.br/

CRU http://www.crugroup.com/

SNL http://www.snl.com/

BROOKHUNT http://www.brookhunt.com

SCIENCEDIRECT http://www.sciencedirect.com/

SCOPUS http://www.scopus.com/home.url

U.S. CENSUS BUREAU http://www.census.gov/

U.S. GEOLOGICAL SURVEY http://www.usgs.gov/

STEEL BUSINESS BRIEFING https://www.steelbb.com/pt/?PageID=1

NASA http://www.nasa.gov/

ESRI http://www.esri.com/

GREENPEACE http://www.greenpeace.org/brasil/pt/

FRASER INSTITUTE https://www.fraserinstitute.org/

WIKIPEDIA http://www.wikipedia.org/

identiﬁed in the literature review and had four layers:

data, wrapper, integration and presentation.

The data layer was implemented in MS-SQL

Server 2012. This implementation enabled creation

of the dataset which was used later to validate com-

petency questions. Dataset was populated with infor-

mation gathered from various sources, as identiﬁed

on focus group. From all information identiﬁed dur-

ing focus group, only those available on web in open

data format were selected to compound this dataset.

In this study, both the wrapper and the integra-

tion layers were implemented through D2RQ Plat-

form, which is a system for accessing relational

databases as virtual, read-only RDF graphs. D2RQ

Platform has D2R Server, which is a tool for pub-

lishing the content of relational databases on the Se-

mantic Web. (Cyganiak, 2012). After installing and

conﬁguring D2RQ Platform, the dataset presented

in Data Layer section was made available through

SPARQL endpoint.

Finally, the presentation layer was implemented.

A web application called DB4Trading was built so

that users could validate data from Semantic Reposi-

tory using their own criteria. From the weight deﬁned

for each one of the variables in the categories area, the

application was designed to identify countries whose

information were more adherent. In order to identify

countries, we used a speciﬁc Google API capable to

highlight the country polygon. DB4Trading was de-

signed to calculate the color of countries, marking in

red those classiﬁed as more relevant and leaving in

blank those classiﬁed as less relevant.

3 RESULTS

Initially, we identiﬁed that enterprises are using Se-

mantic Web technologies for support their operations.

However, according to studies, enterprises still be-

lieve that Semantic Web technologies are complex

and require high specialized teams. Studies also

pointed out that there is a pattern regarding the frame-

works used for implementing Semantic Web in en-

terprises. This common framework enables interlink-

ing of both internal and external data sources and was

used as the conceptual framework.

Based on focus group, we also identiﬁed sixty-

seven variables to support competitive intelligence,

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412

from which twelve are generated internally and ﬁfty-

ﬁve collected from external databases. We created

categories to organize variables based on Nettleton.

(Nettleton, 2014). From all information identiﬁed

during focus group, only those available on web in

open data format were selected to compound the

prototype’s dataset. This categorization was per-

formed following guidelines proposed by Berners-

Lee. (Berners-Lee, 2009).

To validate the conceptual framework we decided

to run speciﬁc queries against dataset and also create

speciﬁc scenarios in visualization application. First

part of the validation aims to show that the dataset

was made available in RDF and was accessible via

SPARQL queries. Second part of the validation aims

to show that the proposed framework was able to an-

swer business needs by integrating and presenting in-

formation from multiple data sources.

3.1 Semantic Repository

SPARQL queries were performed against dataset to

validate the framework. The competency questions

presented next were considered. Figure 1 presents the

SPARQL queries results.

1. What are the countries with the highest human de-

velopment index (HDI)?

2. What are the countries with the highest numbers

of hospital beds?

3. What are the countries with the highest export

trade volume?

4. What are the countries with the lowest tax bur-

dens?

5. What are the countries with the lowest time for

starting a business?

3.2 Visualization Application

In order to address requirements of competitive intel-

ligence professionals who attended the focus group,

we created three scenarios according to the focus

group professionals specialties:

1. Scenario 1 - Engineering perspective, considering

infrastructure variables as more relevant.

2. Scenario 2 - Strategic Management perspective,

considering projection variables as more relevant.

3. Scenario 3 - Health and Environment perspective,

considering human variables as more relevant.

Figure 2 presents results obtained for the ﬁrst sce-

nario.

4 DISCUSSION

It is hoped that this research may contribute to the

clariﬁcation of concepts and technologies related to

linked data, facilitating dissemination and usage in

other ﬁelds, including in corporate environments.

Also, the results of this research can contribute for

reducing costs to obtain information beyond organi-

zation boundaries, since currently, a major effort is

carried out in order to index and normalize informa-

tion gathered from multiple data sources. Finally,

governments and other institutions may also be en-

couraged to release their information using open data

patterns in order to increase the percentual of infor-

mation shared globally.

5 RELATED WORK

Chen, Chau e Zeng introduce the Competitive In-

telligence Spider, a tool responsible for performing

real-time collection of Web pages from sites speci-

ﬁed by the user and applying indexing and catego-

rization analysis on the documents collected. (Chen

et al., 2002). These authors concluded that there ex-

ists strong evidence in support of the potentially sig-

niﬁcant value of applying the CI Spider approach in

CI applications.

Yang et al evaluate 14 existent text mining and

visualization tools. (Yang et al., 2008). In this

study, each tool is discussed in some detail and their

strengths and potential limitations are identiﬁed from

a patent analysis perspective. Authors observed that

tools able to deal with unstructured data are the most

ﬂexible due to their capabilities to process a broad

range of data sources and use of advanced semantic

technologies.

Ferrara et al provide a structured and comprehen-

sive overview of the research in Web Data Extraction

ﬁeld and also provide an overview of most recent re-

sults in the literature. (Ferrara et al., 2012). In this

study, authors recognize that Web Data Extraction is

an important problem that had been studied by means

of different scientiﬁc tools and in a broad range of

applications. According to the study, the linkage of

datasets coming from independent Web platforms fu-

els novel scientiﬁc applications.

The work of Hu and Svensson is directly related to

this study because that work aims to integrate external

and internal data sets to provide strategical informa-

tion. (Hu and Svensson, 2010). Their work also ap-

proaches the existence of connection to multiple data

sources, the possibility to perform queries consider-

ing data from multiple data sources.

SupportingCompetitiveIntelligencewithLinkedEnterpriseData

413

Question #1 Question #2 Question #3 Question #4 Question #5

Figure 1: SPARQL Queries Results.

Figure 2: DB4Trading Scenario 1 - Results.

6 CONCLUSION

In this study we analyzed the application of linked

data technologies into gathering and integrating data

generated both inside and outside an organization

for supporting the competitive intelligence process.

Firstly, we outlined a conceptual framework. Next,

we identiﬁed competency questions, that is, variables

and data sources that were used to build and validate

the framework. Next, we built a prototype for inte-

grated data visualization, based on proposed frame-

work. Finally, we tested and validated conceptual

framework, using competency questions.

Based on the results of this research, we con-

cluded that it is possible to use Semantic Web tech-

nologies to gather and distribute information for ex-

ternal environment analysis. Based on systematic lit-

erature review, we concluded that enterprises are al-

ready using Semantic Web technologies for support

their operations, specially for internal data sources

integration. However, enterprises still believe that

Semantic Web technologies are complex and require

high specialized teams.

6.1 Limitations

Regarding the Systematic Literature Review, we

can point out the following limitations. First, only

papers written in English had been considered. Ad-

ditionally, during data extraction stage, it was neces-

sary to interpret the subjective information provided

by studies. Another potential threat to validity is the

natural limitation of search engines. A Focus Group

also has several limitations. Firstly, focus groups are

susceptible to facilitator bias. Secondly, the discus-

sions can be sidetracked or dominated by a few vocal

individuals. Finally, information generated in focus

group often has limited generalization to a whole pop-

ulation.

Regarding the Semantic Repository, only vari-

ables available in open data format were selected and

related to entities. Also, no crawler was built to up-

date the data after the information was inserted into

the database. Regarding the Visualization Applica-

tion we point out the following limitations. Although

the site is dynamically constructed from the data ﬁles,

no mechanism was generated to automate the export

of data from the database to the ﬁles. Another lim-

itation is related to countries polygon: for unknown

reasons, the Google API was not able to create the

polygon for the following countries: Tanzania, Sene-

gal, Chile, French Guiana and North Korea.

6.2 Future Work

Although this study is limited to the examination of

country data, the Semantic Repository was designed

to allow the creation of classes to represent regions,

cities, neighborhood, and also not geo-referenced en-

tities. Further studies could consider the usage of the

Semantic Repository generated in this study to create

entities and variables applicable to other areas.

The Visualization Application (DB4Trading) gen-

erated in this research could incorporate some im-

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

414

provements: The tool should allow users to assign

scores to each of the data sources because there is a

consensus between participants that credibility may

vary according to the data source. The tool also could

save different scenarios’s settings, allowing users to

retrieve their preferences.

ACKNOWLEDGEMENTS

This Work is partially supported by the Brazilian

Funding Agencies FAPEMIG, CAPES and CNPq.

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