A Cognitive Information Retrieval Using POP Inference Engine Approaches

The focus of this chapter is to design a cognitive information retrieval (CIR) framework using inference engine (IE). IE permits one to analyze the central concepts of information retrieval: information, information needs, and relevance. The aim is to propose an inference engine in which adequate user preferences are considered. As the cognitive inference engine (CIE) approach is involved, the complex inquiries are required to return more important outcomes as opposed to customary database questions which get irrelevant and unsolicited responses or results. The chapter highlights the framework of a cognitive rule-based engine in which preference queries are dealt with while keeping in mind the intention of the user, their performance, and optimization. A Cognitive Information Retrieval Using POP Inference Engine Approaches

[1]  Chokri Ben Amar,et al.  Personalizing information retrieval: A new model for user preferences elicitation , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[2]  Amanda Spink,et al.  A multitasking framework for cognitive information retrieval , 2005 .

[3]  E. F. Codd,et al.  A relational model of data for large shared data banks , 1970, CACM.

[4]  Sebastian Stober,et al.  Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative , 2017, Front. Psychol..

[5]  Diane Kelly,et al.  Methods for Evaluating Interactive Information Retrieval Systems with Users , 2009, Found. Trends Inf. Retr..

[6]  Vagelis Hristidis,et al.  PREFER: a system for the efficient execution of multi-parametric ranked queries , 2001, SIGMOD '01.

[7]  P. Willett,et al.  An Introduction to Algorithmic and Cognitive Approaches for Information Retrieval , 1995 .

[8]  Tefko Saracevic,et al.  RELEVANCE: A review of and a framework for the thinking on the notion in information science , 1997, J. Am. Soc. Inf. Sci..

[9]  Wolf-Tilo Balke,et al.  A Roadmap to Personalized Information Systems by Cognitive Expansion of Queries , 2002 .

[10]  Russell L. Ackoff,et al.  Creativity in problem solving and planning: a review , 1981 .

[11]  M. Hertzum,et al.  Information retrieval systems for professionals: a case study of computer supported legal research , 1993 .

[12]  Mark Baillie,et al.  The relative effects of knowledge, interest and confidence in assessing relevance , 2007, J. Documentation.

[13]  Michael B. Eisenberg Measuring relevance judgments , 1988, Inf. Process. Manag..

[14]  Pia Borlund,et al.  The concept of relevance in IR , 2003, J. Assoc. Inf. Sci. Technol..

[15]  Stefano Mizzaro Relevance: the whole history , 1997 .

[16]  Christos Faloutsos,et al.  Access methods for text , 1985, CSUR.

[17]  N. L. Griffin,et al.  A rule-based inference engine which is optimal and VLSI implementable , 1989, [Proceedings 1989] IEEE International Workshop on Tools for Artificial Intelligence.

[18]  Henda Hajjami Ben Ghézala,et al.  Towards a dynamic and polarity-aware social user profile modeling , 2016, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

[19]  Elaine Toms,et al.  WiIRE: the Web interactive information retrieval experimentation system prototype , 2004, Inf. Process. Manag..

[20]  Henda Ben Ghezala,et al.  Social Information Retrieval and Recommendation: state- of-the-art and future research [Recherche d'Information Sociale et Recommandation: Etat d'art et travaux futurs] , 2019, ARIMA J..

[21]  Jaana Kekäläinen,et al.  Using graded relevance assessments in IR evaluation , 2002, J. Assoc. Inf. Sci. Technol..

[22]  Pertti Vakkari,et al.  The influence of relevance levels on the effectiveness of interactive information retrieval , 2004, J. Assoc. Inf. Sci. Technol..

[23]  Václav Snásel,et al.  User Profiles Modeling in Information Retrieval Systems , 2010, Emergent Web Intelligence.

[24]  Ian Ruthven,et al.  Integrating approaches to relevance , 2005 .

[25]  Abdul Wahid,et al.  A REVIEW OF THE COGNITIVE INFORMATION RETRIEVAL CONCEPT, PROCESS AND TECHNIQUES , 2013 .

[26]  Joemon M. Jose,et al.  How users assess Web pages for information seeking , 2005, J. Assoc. Inf. Sci. Technol..

[27]  T. Saracevic Relevance: A Review of the Literature and a Framework for Thinking on the Notion in Information Science. Part II , 2006 .

[28]  Tefko Saracevic Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance , 2007 .