RCA as a Data Transforming Method: A Comparison with Propositionalisation

This paper aims at comparing transformation-based approaches built to deal with relational data, and in particular two approaches which have emerged in two different communities: Relational Concept Analysis (RCA), based on an iterative use of the classical Formal Concept Analysis (FCA) approach, and Propositionalisation coming from the Inductive Logic Programming community. Both approaches work by transforming a complex problem into a simpler one, namely transforming a database consisting of several tables into a single table. For this purpose, a main table is chosen and new attributes capturing the information from the other tables are built and added to this table. We show the similarities between those transformations for what concerns the principles underlying them, the semantics of the built attributes and the result of a classification performed by FCA on the enriched table. This is illustrated on a simple dataset and we also present a synthetic comparison based on a larger dataset from the hydrological domain.

[1]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[2]  Emma Tonkin,et al.  Analyzing Clusters and Constellations from Untwisting Shortened Links on Twitter Using Conceptual Graphs , 2013, ICCS.

[3]  Hernán Astudillo,et al.  A Conceptual-KDD Approach and its Application to Cultural Heritage , 2013, CLA.

[4]  Amedeo Napoli,et al.  An FCA Framework for Knowledge Discovery in SPARQL Query Answers , 2013, SEMWEB.

[5]  John F. Sowa,et al.  Conceptual Structures: Fulfilling Peirce's Dream , 1997, Lecture Notes in Computer Science.

[6]  Martin Trnecka,et al.  Boolean Factor Analysis of Multi-Relational Data , 2013, CLA.

[7]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[8]  William M. Tepfenhart,et al.  Conceptual Structures: Standards and Practices , 1999, Lecture Notes in Computer Science.

[9]  Amedeo Napoli,et al.  Querying Relational Concept Lattices , 2011, CLA.

[10]  Rudolf Wille,et al.  The Lattice of Concept Graphs of a Relationally Scaled Context , 1999, ICCS.

[11]  Rudolf Wille,et al.  Conceptual Graphs and Formal Concept Analysis , 1997, ICCS.

[12]  Filip Železný,et al.  HiFi: Tractable Propositionalization through Hierarchical Feature Construction , 2008 .

[13]  Karl Erich Wolff,et al.  Relational Scaling in Relational Semantic Systems , 2009, ICCS.

[14]  Gerd Stumme,et al.  Conceptual Structures: Common Semantics for Sharing Knowledge. Proc. , 2005 .

[15]  Amedeo Napoli,et al.  Relational concept analysis: mining concept lattices from multi-relational data , 2013, Annals of Mathematics and Artificial Intelligence.

[16]  Jens Kötters,et al.  Concept Lattices of a Relational Structure , 2013, ICCS.

[17]  Gerd Stumme,et al.  Conceptual Knowledge Discovery and Data Analysis , 2000, ICCS.

[18]  Amedeo Napoli,et al.  Soundness and Completeness of Relational Concept Analysis , 2013, ICFCA.

[19]  Karell Bertet,et al.  Some Links Between Decision Tree and Dichotomic Lattice , 2008, CLA 2008.

[20]  Florence Le Ber,et al.  Identifying Ecological Traits: A Concrete FCA-Based Approach , 2009, ICFCA.

[21]  Franz Baader,et al.  A Finite Basis for the Set of EL-Implications Holding in a Finite Model , 2008, ICFCA.

[22]  Sebastian Rudolph,et al.  Conceptual Structures: Leveraging Semantic Technologies, 17th International Conference on Conceptual Structures, ICCS 2009, Moscow, Russia, July 26-31, 2009. Proceedings , 2009, ICCS.

[23]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[24]  Olivier Ridoux,et al.  Arbitrary Relations in Formal Concept Analysis and Logical Information Systems , 2005, ICCS.

[25]  Bernhard Ganter,et al.  Conceptual Structures: Logical, Linguistic, and Computational Issues , 2000, Lecture Notes in Computer Science.