Improving competitive intelligence for knowledge management systems

Knowledge management encompasses the entire intelligence cycle from planning to reporting. One aspect that is often overlooked or minimised is the inclusion of competitive intelligence. This paper proposes a new concept to fine-tune the process of electronically gathering competitive intelligence – a key activity in knowledge management systems. Most tools are nondiscriminatory in information gathering, and a structured approach is needed to assist managers at all organisational levels in the needs identification process. The proposed multiclass interest profile provides the capability of expanding the coverage of critical intelligence areas to reflect the assorted topics that make up an organisation's information needs. Each component is customisable to make the information that is gathered pertinent to the organisation, and supporting features such as profile expansion and fine-tuning are also incorporated.

[1]  Daniel A. Levinthal,et al.  The myopia of learning , 1993 .

[2]  Mark E. Frisse,et al.  Information retrieval from hypertext: update on the dynamic medical handbook project , 1989, Hypertext.

[3]  Marko Balabanovid,et al.  An Interface for Learning Multi-topic User Profiles from Implicit Feedback , 1998 .

[4]  James Allan,et al.  Incremental relevance feedback for information filtering , 1996, SIGIR '96.

[5]  Adele E. Howe,et al.  How evaluation guides AI research , 1988 .

[6]  Paul R. Cohen,et al.  Toward AI research methodology: three case studies in evaluation , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Gerhard Fischer,et al.  Information access in complex, poorly structured information spaces , 1991, CHI '91.

[8]  Johan P. Olsen,et al.  Ambiguity and choice in organizations , 1976 .

[9]  J. March,et al.  A Behavioral Theory of the Firm , 1964 .

[10]  Hal Berghel,et al.  Cyberspace 2000: dealing with information overload , 1997, CACM.

[11]  I. Nonaka,et al.  The Knowledge Creating Company , 2008 .

[12]  John Grundy,et al.  Static and Dynamic Visualisation of Software Architectures for Component-based Systems , 1998 .

[13]  K. Weick The social psychology of organizing , 1969 .

[14]  B. Gates Business @ the Speed of Thought , 1999 .

[15]  P. Senge The fifth discipline : the art and practice of the learning organization/ Peter M. Senge , 1991 .

[16]  Susan T. Dumais,et al.  Personalized information delivery: an analysis of information filtering methods , 1992, CACM.

[17]  Richard Fikes,et al.  The Ontolingua Server: a tool for collaborative ontology construction , 1997, Int. J. Hum. Comput. Stud..

[18]  Nicola Guarino,et al.  Semantic Matching: Formal Ontological Distinctions for Information Organization, Extraction, and Integration , 1997, SCIE.

[19]  Herbert A. Simon,et al.  The Sciences of the Artificial - 3rd Edition , 1981 .

[20]  Cyril W. Cleverdon,et al.  Factors determining the performance of indexing systems , 1966 .

[21]  Beerud Dilip Sheth,et al.  A learning approach to personalized information filtering , 1994 .

[22]  Myoung-Ho Kim,et al.  Ranking Documents in Thesaurus-Based Boolean Retrieval Systems , 1994, Inf. Process. Manag..

[23]  M. Larson The Rise of Professionalism: A Sociological Analysis , 1977 .

[24]  Robert Kass,et al.  Modeling users' interests in information filters , 1992, CACM.

[25]  Enrico Motta,et al.  A Knowledge-Based News Server Supporting Ontology-Driven Story Enrichment and Knowledge Retrieval , 1999, EKAW.

[26]  M. Guy,et al.  Professionals in Organizations: Debunking a Myth , 1985 .

[27]  H. Simon,et al.  Organizations, 2nd ed. , 1993 .

[28]  J. Herring Key intelligence topics: A process to identify and define intelligence needs , 1999 .

[29]  Daniel A. Levinthal,et al.  A model of adaptive organizational search , 1981 .

[30]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[31]  Susan Jones A thesaurus data model for an intelligent retrieval system , 1993, J. Inf. Sci..

[32]  Roy Rada,et al.  A Graphical Thesaurus-Based Information Retrieval System , 1989, Int. J. Man Mach. Stud..

[33]  Carolyn J. Crouch,et al.  Experiments in automatic statistical thesaurus construction , 1992, SIGIR '92.

[34]  Louis E. Yelle THE LEARNING CURVE: HISTORICAL REVIEW AND COMPREHENSIVE SURVEY , 1979 .

[35]  John R. Anderson The Adaptive Character of Thought , 1990 .

[36]  Douglas W. Oard,et al.  Using Implicit Feedback for User Modeling in Internet and Intranet Searching ϕ , 2000 .

[37]  Micheline Hancock-Beaulieu,et al.  An Evaluation of Automatic Query Expansion in an Online Library Catalogue , 1992, J. Documentation.

[38]  R. Anthony,et al.  Planning and Control Systems: A Framework for Analysis , 1965 .

[39]  Stephen E. Robertson,et al.  On Term Selection for Query Expansion , 1991, J. Documentation.

[40]  K. Weick FROM SENSEMAKING IN ORGANIZATIONS , 2021, The New Economic Sociology.

[41]  Thomas R. Gruber,et al.  The Role of Common Ontology in Achieving Sharable, Reusable Knowledge Bases , 1991, KR.

[42]  E. Johnsen Richard M. Cyert & James G. March, A Behavioral Theory of The Firm, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1963, 332 s. , 1964 .

[43]  Susan T. Dumais,et al.  The vocabulary problem in human-system communication , 1987, CACM.

[44]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[45]  Peter Willett,et al.  Effectiveness of query expansion in ranked-output document retrieval systems , 1992, J. Inf. Sci..

[46]  L. Argote,et al.  The persistence and transfer of learning in industrial settings , 1990 .

[47]  Mark Sanderson,et al.  Word sense disambiguation and information retrieval , 1994, SIGIR '94.

[48]  J. March Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.