Why and How Knowledge Discovery Can Be Useful for Solving Problems with CBR

In this talk, we discuss and illustrate links existing between knowledge discovery in databases (KDD), knowledge representation and reasoning (KRR), and case-based reasoning (CBR). KDD techniques especially based on Formal Concept Analysis (FCA) are well formalized and allow the design of concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge base organization and ontology engineering. More generally, they can be used for representing knowledge and reasoning in knowledge systems and CBR systems as well.

[1]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

[2]  Markus Völter,et al.  Model-Driven Software Development: Technology, Engineering, Management , 2006 .

[3]  Mathieu d'Aquin,et al.  Case Base Mining for Adaptation Knowledge Acquisition , 2007, IJCAI.

[4]  L. Beran,et al.  [Formal concept analysis]. , 1996, Casopis lekaru ceskych.

[5]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[6]  Jean-François Boulicaut,et al.  Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings , 2009, IDA.

[7]  Pedro A. González-Calero,et al.  Formal concept analysis as a support technique for CBR , 2001, Knowl. Based Syst..

[8]  Amedeo Napoli,et al.  Constructing Iceberg Lattices from Frequent Closures Using Generators , 2008, Discovery Science.

[9]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[10]  Amedeo Napoli,et al.  A SMOOTH INTRODUCTION TO SYMBOLIC METHODS FOR KNOWLEDGE DISCOVERY , 2005 .

[11]  Malcolm P. Atkinson,et al.  Issues Raised by Three Years of Developing PJama: An Orthogonally Persistent Platform for Java , 1999, ICDT.

[12]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[13]  Zainab Assaghir,et al.  Embedding tolerance relations in formal concept analysis: an application in information fusion , 2010, CIKM '10.

[14]  Bernhard Ganter,et al.  Pattern Structures and Their Projections , 2001, ICCS.

[15]  Zainab Assaghir,et al.  Managing Information Fusion with Formal Concept Analysis , 2010, MDAI.

[16]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[17]  M.R. Hacene,et al.  Ontology Learning from Text Using Relational Concept Analysis , 2008, 2008 International MCETECH Conference on e-Technologies (mcetech 2008).

[18]  Pedro A. González-Calero,et al.  Classification Based Retrieval Using Formal Concept Analysis , 2001, ICCBR.

[19]  Zainab Assaghir,et al.  Two Complementary Classification Methods for Designing a Concept Lattice from Interval Data , 2010, FoIKS.

[20]  Nicolas Pasquier,et al.  Pruning closed itemset lattices for associations rules , 1998, BDA.

[21]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[22]  Sergei O. Kuznetsov,et al.  Galois Connections in Data Analysis: Contributions from the Soviet Era and Modern Russian Research , 2005, Formal Concept Analysis.

[23]  Rudolf Wille,et al.  Why can concept lattices support knowledge discovery in databases? , 2002, J. Exp. Theor. Artif. Intell..

[24]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[25]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[26]  Gerd Stumme,et al.  Conceptual Structures: Broadening the Base , 2001, Lecture Notes in Computer Science.

[27]  Steffen Staab,et al.  International Handbooks on Information Systems , 2013 .

[28]  Sergei O. Kuznetsov,et al.  Pattern Structures for Analyzing Complex Data , 2009, RSFDGrC.

[29]  Amedeo Napoli,et al.  Two FCA-Based Methods for Mining Gene Expression Data , 2009, ICFCA.

[30]  Sergei O. Kuznetsov,et al.  Comparing performance of algorithms for generating concept lattices , 2002, J. Exp. Theor. Artif. Intell..

[31]  Amedeo Napoli,et al.  A Proposal for Combining Formal Concept Analysis and Description Logics for Mining Relational Data , 2007, ICFCA.

[32]  Sergei O. Kuznetsov,et al.  Learning Closed Sets of Labeled Graphs for Chemical Applications , 2005, ILP.

[33]  Ollivier Haemmerlé,et al.  Conceptual Structures: Knowledge Visualization and Reasoning, 16th International Conference on Conceptual Structures, ICCS 2008, Toulouse, France, July 7-11, 2008, Proceedings , 2008, ICCS.

[34]  Amedeo Napoli,et al.  Efficient Vertical Mining of Frequent Closures and Generators , 2009, IDA.

[35]  Amedeo Napoli,et al.  PACTOLE: A Methodology and a System for Semi-automatically Enriching an Ontology from a Collection of Texts , 2008, ICCS.

[36]  Amedeo Napoli,et al.  Many-Valued Concept Lattices for Conceptual Clustering and Information Retrieval , 2008, ECAI.

[37]  Ronald J. Brachman,et al.  The Process of Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[38]  Amedeo Napoli,et al.  Using Domain Knowledge to Guide Lattice-based Complex Data Exploration , 2010, ECAI.

[39]  Aldo Gangemi,et al.  Knowledge Engineering: Practice and Patterns, 16th International Conference, EKAW 2008, Acitrezza, Italy, September 29 - October 2, 2008. Proceedings , 2008, EKAW.

[40]  Henri Cohen,et al.  Handbook of categorization in cognitive science , 2005 .

[41]  Amedeo Napoli,et al.  Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies , 2008, EKAW.

[42]  Amedeo Napoli,et al.  First Elements on Knowledge Discovery Guided by Domain Knowledge (KDDK) , 2006, CLA.

[43]  Gerd Stumme,et al.  Mining frequent patterns with counting inference , 2000, SKDD.