modelling document usage in competitive intelligence process

Transition from Observation to Knowledge to Intelligence TOKI2014

1

Modelling Document Usage in Competitive Intelligence

Process

L. A. AkanbiB. S. Afolabi

A. DavidE. R. Adagunodo

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2

Competitive Intelligence

Document Annotation

Document Usage Model

Model Evaluation

Conclusion

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Outline of the Presentation


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Competitive Intelligence is a process that involves:

gathering, analysis and processing of environmental information

Assisting strategic decision making (Trigo et al., 2007; Dishman and Calof, 2008)

The process of information gathering is legal and ethical (SCIP, 2012)

The environment could be external or internal to the organization.

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Competitive Intelligence


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Identification and specification of decision problem

Transformation of decision problem into information search problem

Identification and validation of sources Collection and validation of necessary

information

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Stages in Competitive Intelligence Process


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Processing and calculating of necessary indicators for decision making

Interpretation of indicators Decision making for the resolution of

identified problem (Chen et al., 2002; Odumuyiwa and David, 2008)

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Stages in Competitive Intelligence Process cont’d


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Competitive Intelligence Process

Identification of Decision Problem (DP)

Transformation of DP into Information Search Problem

Identification of Relevant

Sources of Information

Collection of Relevant Information

Analysis of Collected Information to extract indicators for decision making

Interpretation of Indicators

Decision Making

Documents Sources

Terms in the documents

< represented as >

< collect from>


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Competitive Intelligence Process



Identification of Relevant

Sources of Information




Decision Making

Documents Sources

Usage descriptors (U, P, D & E)


< represented as >

< represented as >

< collect from>


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Annotation can be seen as simply information about the document, assigned by a process or human, after the original creation of the document (Ogilvie, 2010)

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Document Annotation


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AMIE- Annotation Model for Information Exchange – (Robert and David, 2006)◦ The model was reported to have been conceived with the objective

of information sharing and reuse

The use of annotation process as an indexing approach that allows the users to identify and regroup documents or their sub-elements under a particular use context was employed in Maghreb and David (2008)

AMTEA -Annotation Model and Tools for Economic Actors – (Okunoye et al. (2010)◦ proposed the use of annotation representation as Attribute-Value-

Pair (AVP) as a medium of capturing EI actors’ interpretation to document of interest.

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Some Applications of Document Annotation


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All search activities for information is associated with a decision problem.

Existing techniques for integrating context into document index are based on inference methods using statistical or linguistic methods.

These methods cannot capture the usage of information by the end-user.

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Problem Statement


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The document usage is modelled as a function of the following attributes:

DU = f(U, P, D, R)

where:

DU = Document usage U = User P = decision Problem D = Document representation R = document degree of relevance to the

resolution of decision problem


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The User attribute (U) is composed of the following values:

U = {id, status, estab}where:

id = a unique identity allocated to the user during the course of registering to use the system status = the current position of the user in the establishment or organization.

estab = the users’ establishment information such as type of establishment (private or public) and name of the establishment.

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13

Taking cue from Bouaka (2004), we describe the decision problem P as:

P = {Obj, Signal, Hyp} where:

Obj = the stake object attribute.Signal = the stake signal i.e. the level of the user’s

knowledge about the decision problem. Hyp = the stake hypothesis i.e. what the establishment

stand to gain or lose.

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The attribute D of the DU is also formally define as:

D = {id, title, abs}

where:id = the unique identity of the document. This

is automatically assigned by the system.title = title of the documentabs = document abstract or summary.

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The attribute R of the DU is document’s degree of relevance to the resolution of the decision problem. It s a function of the following attributes:

R = f(UR, UY, NP)

where:UR = User’s rating of document’s relevance to

decision problem UY = User’s number of years in the establishment

NP = Number of similar problems handled in the past

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where

is the users of the system is the document’s degree of relevance to decision problem (DP) is the terms from the DP description is the terms from the documents is the number of users in the system is the number of terms in the document space

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Items and are used to generate value added information

Items and are used to generate the usage index (UI).

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Vector Space Model was used to represent the document collection in the document space

Calculate the similarity between Decision Problem and key-term based document index

Calculate the similarity between decision problem and usage based document index

Compare the two results

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Model Evaluation


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Calculating the Similarity between Decision Problem and Documents

where: j = 1 …….. n (n= number of document in the document collection space). t = the number of terms in the vector space. dj = jth document vector in the document space P = decision problem transform to document vector in the document collection space wij = the weight of term i in document j. wi,q = the weight of term i in the query q (i.e. the decision problem).

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Sample Document and DP

d1 = AMIE: An annotation model for information research

d2 = AMTEA: Tool for Creating and Exploiting Annotations in the Context of Economic Intelligence (Competitive Intelligence)

d3 = What Is a "Document"?

d4 = CI Spider: a tool for competitive intelligence on the Web

d5 = Dynamic Knowledge Capitalization through Annotation among Economic Intelligence Actors in a Collaborative Environment

d6 = Design and Development of a Model for Generating and Exploiting Annotation in the Context of Economic Intelligence

dp = Development of model and system to create usage descriptors for documents. Then writing of Doctorate thesis on the model and the system.


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Document Index based on key terms

d1 = AMIE: annotation model information research

d2 = AMTEA: Tool Creating Exploiting Annotations Context Economic Intelligence Competitive Intelligence

d3 = Document

d4 = CI Spider tool competitive intelligence Web

d5 = Dynamic Knowledge Capitalization Annotation Economic Intelligence Actors Collaborative Environment

d6 = Design Development Model Generating Exploiting Annotation Context Economic Intelligence


22

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Document Index in Vector Space


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Document Usage Index in Vector Space


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Similarity between Decision Problem and Documents

Documents

Term-based Index

Term + Usage

Based Indexd1 0.23094 0.86141d2 0.07454 0.76714d3 0.25820 0.97373d4 0.00000 0.84515d5 0.00000 0.79057d6 0.25820 0.84853


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In Graphical Form


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Threshold is usually set (e.g. 0.3) for documents to be considered relevant to queries.

Documents d4 and d5 will not be considered relevant to the query

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Result Analysis


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The IR component of the CI process is very crucial to speedy and easy resolution of DPs.

This work sought to keep track of what documents have been used for and incorporate it into the document representation scheme to enhance the quality of IR stage of the CI process.

The document usage model presented is used to preserve the effort, the decision makers put to discover relevant documents for the resolution of DPs

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Conclusion


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Thanks for Listening

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Ph.D. Qualifying Exam 3007 April 2014

A sample information retrieval (search) analysis

Ph.D. Qualifying Exam 3107 April 2014

A sample information retrieval (search) analysis

Ph.D. Qualifying Exam 32

indexing is by keyterms generated from the documents only

terms usually have different concept users’ inability to properly and correctly

translate their needs into query users' lack of adequate knowledge about

how the system functions

07 April 2014

Some causes of the perceived noise in the information retrieved


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Competitive Intelligent System Architecture

? (a, b)

? (a, b)

Results

Value Added Information

Decision

mapping

Selection

Analysis Interpretation

Demand

Usersa) Decision makerb) Watcher

Cognitive Process- Observation- Elementary abstraction- Reasoning- Creativity

Information Base

? ( b)

? (a, b)

Information World

(Source: Thiery and David, 2002)Proposed


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Architecture of the Proposed System

Existing

ResultsValue Added Information Decision

Mapping

Selection

Analysis Interpretation

DP descriptors DocumentsDocuments

Documents

Request Information Base

Document usage index

Document index

Information World

User Information


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Is it possible to model document usage computationally?

Can the document usage model be incorporated into document representation for modern information retrieval system (IRS)?

Can documents usage model enhance accessibility to documents required for the resolution of decision problem?

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Research Questions


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The Philosophy of this work is derived from the gratification theory (Fiske, 1990)

Given the history of the usage of a document in addition to author’s defined key terms, there is the likelihood to increase the rate of accessibility to the documents in the nearest future.

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Research Theory and Philosophy


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The aim of this work is to create a computing environment in term of software that will allow for creation and exploration of document usage

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Research Aim


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Representing document with terms generated from the document itself and previous usage will enhance the rate of accessibility to the document

Thereby reducing the amount of effort that would be required by users to identify documents that are relevant to their information needs

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Research Justification


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The specific objectives are to: i. formulate a model that augments document index with usage

ii. design a system based on the model formulated in (i)

iii. implement the system designed in (ii) and iv. evaluate the system

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Research Objectives


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Indexing by latent semantic analysis (Deerwester

et al. 1990) involves the use of Singular Value Decomposition (SVD) to analyse the term-document matrix. Modeling the underlying term-to-document association patterns is the key in this approach

Context Based Indexing in Search Engines using Ontology (Gupta and Sharma, 2010) involves the use of context repository, thesaurus and ontology repository to build the document index

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Literature Review


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Okunoye and Uwadia (2011) Designed and Developed a Model for Generating and Exploiting Annotation in the Context of Economic Intelligence

Proposed representation of annotation as attribute value pair rather than as atomic object, to allow the user to carry out annotation on documents with their specified attribute and value.

Other related works are here

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Literature Review


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i. Document usage model was created with the use of Attribute Value Pair technique of document annotation and Vector Space Model of Information Retrieval

iii. A system that integrates document usage with document index based on (i) was produced with the use of Unified Modelling Language (UML 2.0)

iv. The prototype of the system was implemented with the use of PhP and MySql technology

v. The system was evaluated with the use similarity measure (i.e. Euclidean distance) between sample decision problems and documents

Form A20-22, August 2014

Research Methodology


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L. A. Akanbi and E. R. Adagunodo (2013) A framework for linking Documents with its usage within the Context of Competitive Intelligence. 9th International Society for Knowledge Organization Conference. Paris, France. 10 -13 October 2013.- PUBLISHED

L. A. Akanbi, B. S. Afolabi, E. R. Adagunodo and A. David. Modelling Document Usage in Competitive Intelligence Process. TOKI Conference (2014) University of Lagos. Nigeria. 20 – 22 August 2014 -ACCEPTED

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Publications


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Other Information


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The process of identifying useful documents that contribute to the resolution of decision problems are often time-consuming and uneconomical.

Existing search systems do not incorporate document usage into document indexing.

To enhance accessibility to documents, there is the need for a system to augment document index with document usage. Hence, this study.

Back

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Statement of the Problem


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Attribute Value Pair, a document annotation technique will be used to formulate a model that augments document index with document usage. A software system will be designed based on the model formulated with the use of Unified Modelling Language (UML 2.0). The prototype of the system designed will be implemented with PhP and MySQL technology. To evaluate the performance of the system, data on document usage will be collected through questionnaire administration and guided interview. The data will be collected from twenty (20) selected postgraduate students (M.Sc. and Ph.D.) in various departments in the faculty of Technology who are at different stages of their thesis. Similarity measure based on Euclidean distance between identified relevant documents by the respondents and their decision problems (i.e. research problems) will be used to evaluate the system.

Back

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Research Methodology


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CI Process incorporating Document Usage



Identification of Relevant Sources of

Information




Decision Making

Documents Sources

Usage descriptors (U, P, D & E)


< represented as >

< represented as >

< collect from>


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Number of Years

TR L MD H

0 1 2 3 4 5

1

µ(EL)


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User’s Rating of Document Relevance to DP Resolution

NR SR R VR

0 1 2 3 4 5

1

µ(EL)


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Number of Similar Problem Solved

VS S M H

0 1 2 3 4 5 Number of Similar Problem handled in the past

1

µ(NSP)

VH


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Document Relevance to DP

NU SU U VU

0 1 2 3 4 5 Crisp value for R

1µ(R)


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Taking our research work as a Decision Problem

Selecting six documents that have been used during the course of the study (document title used for analysis)

P = stake_obj = {Development of model and system to create usage descriptors for documents. Then writing of Doctorate thesis on the model and the system}

doci = (ti ), where t = title of the document

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Sample Results


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Document DDU

d1 0.80000

d2 0.80000

d3 0.70000

d4 0.70000

d5 0.70000

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DDU for Respondent-1

Table 4.8: Document Degree of Usefulness (DDU) for (a) Respondent-1, (b) Respondent- 3, (c) Respondent-4, (d) Respondent-6, (e) Respondent-12(a)


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Term-based index document representation

A document is represented as a vector where D is the vector of all the words in a document. d is a subset D.

D = (w1, w2, w3 w4 ….. wn)

wi = word i in document D.

d = (t1, t2, t3, t4 ….. tn)

ti D = terms in the document representation

Usage-based index document representation

Two components to represent document D.

a) d = (t1, t2, t3, t4 ….. tn), ti D

b) decision problem stake object represented as vector P containing terms used to describe the

problem. P = (k1, k2, k3, k4, …. kn)

New document representation dr is given by

dr =(d . P)

=( t1, t2, t3, t4 ….. tn . k1, k2, k3, k4, …. kn).


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CIDUCE Interface for Normal Search Operation


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CIDUCE Interface for Normal Search Operation


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Data collection through questionnaire administration to selected postgraduate students in the faculty.

The information sought includes title of their thesis, their brief description of their research work and some documents that they considered very useful to the work.

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Data for the Model Evaluation


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Bouaka, N. (2004). Développement d'un modèle pour l'explicitation d'un problème décisionnel: un outil d'aide à la décision dans un contexte d'intelligence economique . DoctoralThesis Université Nancy 2, Nancy. France.

Briet, S. (1951). Qu’est-ce que la documentation. Edit, Paris. Éditions Documentaires. Buckland, M. K. (1991). Information as Thing. Journal of the American Society of

Information Science. 42(5) Pp351-360. Buckland, M. K. (1997). What is a “document”?, Journal of American Society of

Information Science. Vol. 42, No. 5, Pp 351-360. Chen, H., Chau, M. and Zeng, D. (2002). CI Spider: a tool for competitive intelligence on

the Web. Elsevier Journal of Decision Support System.Vol.34, Issue 1. Pp 1-17. David, A. (2008) Problematique emergentes dans les sciences de l’information, chapitre 8

sur L’information pertinente en intelligence economique, ISBN 978-2-7462-2110-9. Eds Hermes-Lavoisier, 2008.

Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. and Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of American Society for Information Science. 41(6), Pp 391-407.

Dishman, P. L. and Calof, J. L. (2008). Competitive intelligence: a multiphasic precedent to marketing strategy, European Journal of Marketing, Vol. 42, No 7/8. Pp 766 – 785.

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References


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Fiske, J. (1990). Introduction to communication studies, 2nd ed. London: Routledge. Gupta, D., Bhatia, K. K., and Sharma, A. K. (2009). A novel indexing technique for web documents

using hierarchical clustering. International Journal of Computer Science and Network Security, 9(9). Pp 168 - 175.

Odumuyiwa, V and David, A. (2008). Collaborative Information Retrieval among Economic Intelligence Actors. 4th International Conference on Collaboration Technology, CollabTech 2008, Wakayama Japan. Pp 21-26.

Okunoye, O. B., David, A., and Uwadia, C. (2010). Amtea: Tool for creating and exploring annotations in the context of economic intelligence (Competitive Intelligence). In 11th IEEE International Conference on Information Reuse and Integration (IRI 2010), pages 249-252, Las Vegas, United States.

SCIP, (2012) About SCIP, Strategic and Competitive Intelligence Professional. Available at http://www.scip.org/content.cfm?itemnumber=2214&navItemNumber=492, (Retrieved 15/10/2012).

Thiery O., and David A., (2002). Modélisation de l’utilisateur, Systèmes d’Informations Stratégiques et Intelligence Economique, Revue Association pour le Développement du Logiciel (ADELI), no. 47.

Trigo, M. R., Gouveia, L. B., Quoniam, L., and Riccio, E. L. (2007). Using competitive intelligence as a strategic tool in a Higher Education context, 8th European Conference on Knowledge Management (ECKM).Consorci Escola Industrial de Barcelona (CEIB), Barcelona, Spain. 6-7 September 2007.

Zacklad, M. (2006). Documentarisation processes in Documents for Action (DofA): the status of annotations and associated cooperation technologies. Computer Supported Cooperative Work (CSCW), Vol. 15, No 2-3, Pp 205-228.

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References

http://www.scip.org/content.cfm?itemnumber=2214&navItemNumber=492

modelling document usage in competitive intelligence process

Documents

intelligence toki201420

information exchange

relevant informationanalysis

document ogilvie

manual annotation tools

automatic tools

necessary indicators

automatic suggestions