We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.

@inproceedings{jaschke2013attribute, author = {Jäschke, Robert and Rudolph, Sebastian}, title = {Attribute Exploration on the Web}, editor = {Cellier, Peggy and Distel, Felix and Ganter, Bernhard}, booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis}, year = {2013}, pages = {19--34}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133}, keywords = {2013, acquisition, analysis, attribute, computing, concept, crowdsourcing, data, exploration, fca, formal, human, information, ir, iteg, knowledge, l3s, linked, lod, open, retrieval, search, sparql, web}, abstract = {We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.} }

%0 = inproceedings %A = Jäschke, Robert and Rudolph, Sebastian %B = Contributions to the 11th International Conference on Formal Concept Analysis %D = 2013 %T = Attribute Exploration on the Web %U = http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133

We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.

@inproceedings{doerfel2012publication, author = {Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd}, title = {Publication Analysis of the Formal Concept Analysis Community}, editor = {Domenach, F. and Ignatov, D.I. and Poelmans, J.}, booktitle = {ICFCA 2012}, series = {Lecture Notes in Artificial Intelligence}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2012}, volume = {7278}, pages = {77--95}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2012publication.pdf}, keywords = {2012, analysis, community, concept, fca, home, info20pub, itegpub, l3s, myown, scientometrics}, abstract = {We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.
} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd %B = ICFCA 2012 %C = Berlin/Heidelberg %D = 2012 %I = Springer %T = Publication Analysis of the Formal Concept Analysis Community %U = https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2012publication.pdf

Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.

@incollection{noKey, author = {Poelmans, Jonas and Ignatov, DmitryI. and Viaene, Stijn and Dedene, Guido and Kuznetsov, SergeiO.}, title = {Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research}, editor = {Perner, Petra}, booktitle = {Advances in Data Mining. Applications and Theoretical Aspects}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, year = {2012}, volume = {7377}, pages = {273-287}, url = {http://dx.doi.org/10.1007/978-3-642-31488-9_22}, doi = {10.1007/978-3-642-31488-9_22}, isbn = {978-3-642-31487-2}, keywords = {FCA, IR, Mining, SOTA, Text}, abstract = {Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.} }

%0 = incollection %A = Poelmans, Jonas and Ignatov, DmitryI. and Viaene, Stijn and Dedene, Guido and Kuznetsov, SergeiO. %B = Advances in Data Mining. Applications and Theoretical Aspects %D = 2012 %I = Springer Berlin Heidelberg %T = Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research %U = http://dx.doi.org/10.1007/978-3-642-31488-9_22

@inproceedings{hotho2006information, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Information Retrieval in Folksonomies: Search and Ranking}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2006}, pages = {411-426}, keywords = {FCA, OntologyHandbook, folkrank, folksonomy, graph, information, mining, pagerank, rank, ranking, retrieval, search, seminar2006} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = Proceedings of the 3rd European Semantic Web Conference %D = 2006 %I = Springer %T = Information Retrieval in Folksonomies: Search and Ranking

@incollection{cimiano2004conceptual, author = {Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien}, title = {Conceptual Knowledge Processing with Formal Concept
Analysis and Ontologies}, editor = {Eklund, Peter}, booktitle = {Concept Lattices}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2004}, volume = {2961}, pages = {189--207}, url = {http://dx.doi.org/10.1007/978-3-540-24651-0_18}, isbn = {978-3-540-21043-6}, keywords = {2004, citedBy:doerfel2012publication, fca, icfca, itegpub, knowledge, l3s, myown, processing} }

%0 = incollection %A = Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien %B = Concept Lattices %C = Berlin/Heidelberg %D = 2004 %I = Springer %T = Conceptual Knowledge Processing with Formal Concept
Analysis and Ontologies %U = http://dx.doi.org/10.1007/978-3-540-24651-0_18

@inproceedings{calders02mining, author = {Calders, Toon and Goethals, Bart}, title = {Mining All Non-derivable Frequent Itemsets}, booktitle = {PKDD '02: Proceedings of the 6th European Conference on Principles
of Data Mining and Knowledge Discovery}, publisher = {Springer-Verlag}, address = {London, UK}, year = {2002}, pages = {74--85}, isbn = {3-540-44037-2}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Calders, Toon and Goethals, Bart %B = PKDD '02: Proceedings of the 6th European Conference on Principles
of Data Mining and Knowledge Discovery %C = London, UK %D = 2002 %I = Springer-Verlag %T = Mining All Non-derivable Frequent Itemsets

We introduce the notion of iceberg concept lattices
and show their use in knowledge discovery in
databases. Iceberg lattices are a conceptual
clustering method, which is well suited for analyzing
very large databases. They also serve as a condensed
representation of frequent itemsets, as starting
point for computing bases of association rules, and
as a visualization method for association rules.
Iceberg concept lattices are based on the theory of
Formal Concept Analysis, a mathematical theory with
applications in data analysis, information retrieval,
and knowledge discovery. We present a new algorithm
called TITANIC for computing (iceberg) concept
lattices. It is based on data mining techniques with
a level-wise approach. In fact, TITANIC can be used
for a more general problem: Computing arbitrary
closure systems when the closure operator comes along
with a so-called weight function. The use of weight
functions for computing closure systems has not been
discussed in the literature up to now. Applications
providing such a weight function include association
rule mining, functional dependencies in databases,
conceptual clustering, and ontology engineering. The
algorithm is experimentally evaluated and compared
with Ganter's Next-Closure algorithm. The evaluation
shows an important gain in efficiency, especially for
weakly correlated data.

@article{stumme2002computing, author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi}, title = {Computing iceberg concept lattices with TITANIC}, journal = {Data & Knowledge Engineering}, publisher = {Elsevier Science Publishers B. V.}, address = {Amsterdam, The Netherlands, The Netherlands}, year = {2002}, volume = {42}, number = {2}, pages = {189--222}, url = {http://portal.acm.org/citation.cfm?id=606457}, doi = {10.1016/S0169-023X(02)00057-5}, issn = {0169-023X}, keywords = {citedBy:doerfel2012publication, fca, icfca, itegpub, l3s, myown, titanic}, abstract = {We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.} }

%0 = article %A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi %C = Amsterdam, The Netherlands, The Netherlands %D = 2002 %I = Elsevier Science Publishers B. V. %T = Computing iceberg concept lattices with TITANIC %U = http://portal.acm.org/citation.cfm?id=606457

@inproceedings{burdick01mafia, author = {Burdick, D. and Calimlim, M. and Gehrke, J.}, title = {MAFIA: A maximal frequent itemset algorithm for transactional databases}, booktitle = {Proc. of the 17th Int. Conf. on Data Engineering}, publisher = {IEEE Computer Society}, year = {2001}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Burdick, D. and Calimlim, M. and Gehrke, J. %B = Proc. of the 17th Int. Conf. on Data Engineering %D = 2001 %I = IEEE Computer Society %T = MAFIA: A maximal frequent itemset algorithm for transactional databases

@inproceedings{Bykowski01condensed, author = {Bykowski, Artur and Rigotti, Christophe}, title = {A condensed representation to find frequent patterns.}, booktitle = {PODS}, year = {2001}, url = {http://dblp.uni-trier.de/db/conf/pods/pods2001.html#BykowskiR01}, keywords = {FCA, OntologyHandbook, dblp} }

%0 = inproceedings %A = Bykowski, Artur and Rigotti, Christophe %B = PODS %D = 2001 %T = A condensed representation to find frequent patterns. %U = http://dblp.uni-trier.de/db/conf/pods/pods2001.html#BykowskiR01

@phdthesis{Bastide00, author = {Bastide, Y.}, title = {Data Mining: algorithmes par niveau, techniques d'implementation
et applications}, school = {Université de Clermont-Ferrand II}, year = {2000}, keywords = {FCA, OntologyHandbook} }

%0 = phdthesis %A = Bastide, Y. %D = 2000 %T = Data Mining: algorithmes par niveau, techniques d'implementation
et applications

@inproceedings{boulicaut00approximation, author = {Boulicaut, Jean-Francois and Bykowski, Artur and Rigotti, Christophe}, title = {Approximation of Frequency Queries by Means of Free-Sets}, booktitle = {PKDD '00: Proceedings of the 4th European Conference on Principles
of Data Mining and Knowledge Discovery}, publisher = {Springer-Verlag}, address = {London, UK}, year = {2000}, pages = {75--85}, isbn = {3-540-41066-X}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Boulicaut, Jean-Francois and Bykowski, Artur and Rigotti, Christophe %B = PKDD '00: Proceedings of the 4th European Conference on Principles
of Data Mining and Knowledge Discovery %C = London, UK %D = 2000 %I = Springer-Verlag %T = Approximation of Frequency Queries by Means of Free-Sets

@techreport{Burmeister98, author = {Burmeister, P.}, title = {Formal concept analysis with ConImp : Introduction to the
basic features}, address = {Darmstadt, Germany}, year = {1998}, note = {http://www.mathematik.tu-darmstadt.de/~burmeister/ConImpIntro.ps}, keywords = {FCA, OntologyHandbook} }

%0 = techreport %A = Burmeister, P. %B = } %C = Darmstadt, Germany %D = 1998 %I = %T = Formal concept analysis with ConImp : Introduction to the
basic features} %U =

@inproceedings{Fayet98, author = {Fayet, A. and Giacometti, A. and Laurent, D. and Spyratos, N.}, title = {Découverte de r�gles pertinentes dans les bases de donn�es}, booktitle = {Actes des 14�mes journ�es Bases de Donn�es Avanc�es (BDA'98)}, year = {1998}, pages = {197--211}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Fayet, A. and Giacometti, A. and Laurent, D. and Spyratos, N. %B = Actes des 14�mes journ�es Bases de Donn�es Avanc�es (BDA'98) %D = 1998 %T = Découverte de r�gles pertinentes dans les bases de donn�es

@inproceedings{Brin97b, author = {Brin, S. and Motwani, R. and Silverstein, C.}, title = {Beyond market baskets: Generalizing association rules to correlation}, booktitle = {Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97)}, publisher = {ACM Press}, year = {1997}, pages = {265--276}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Brin, S. and Motwani, R. and Silverstein, C. %B = Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97) %D = 1997 %I = ACM Press %T = Beyond market baskets: Generalizing association rules to correlation

@inproceedings{Brin97a, author = {Brin, S. and Motwani, R. and Ullman, J. D. and Tsur, S.}, title = {Dynamic itemset counting and implication rules for market basket
data}, booktitle = {Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97)}, publisher = {ACM Press}, year = {1997}, pages = {255--264}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Brin, S. and Motwani, R. and Ullman, J. D. and Tsur, S. %B = Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97) %D = 1997 %I = ACM Press %T = Dynamic itemset counting and implication rules for market basket
data

@incollection{Fayyad96c, author = {Fayyad, U. M. and Piatetsky-Shapiro, G. and Smyth, P.}, title = {From data mining to knowledge discovery : An overview}, editor = {Fayyad, U. M. and Piatetsky-Shapiro, G. and Smyth, P. and Uthurusamy, R.}, booktitle = {Advances in Knowledge Discovery and Data Mining}, publisher = {AAAI Press}, year = {1996}, pages = {1--30}, keywords = {FCA, OntologyHandbook} }

%0 = incollection %A = Fayyad, U. M. and Piatetsky-Shapiro, G. and Smyth, P. %B = Advances in Knowledge Discovery and Data Mining %D = 1996 %I = AAAI Press %T = From data mining to knowledge discovery : An overview

@inproceedings{Fayyad96b, author = {Fayyad, U. M. and Piatetsky-Shapiro, G. and Smyth, P.}, title = {Knowledge discovery and data mining : Towards a unifying framework}, booktitle = {Proceedings of the 2nd international conference on Knowledge Discovery
and Data mining (KDD'96)}, publisher = {AAAI Press}, year = {1996}, pages = {82--88}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Fayyad, U. M. and Piatetsky-Shapiro, G. and Smyth, P. %B = Proceedings of the 2nd international conference on Knowledge Discovery
and Data mining (KDD'96) %D = 1996 %I = AAAI Press %T = Knowledge discovery and data mining : Towards a unifying framework

@inproceedings{Feldman95, author = {Feldman, R. and Dagan, I.}, title = {Knowledge discovery in textual databases}, booktitle = {Proceedings of the 1st international conference on Knowledge Discovery
and Data mining (KDD'95)}, publisher = {AAAI Press}, year = {1995}, pages = {112--117}, keywords = {FCA, OntologyHandbook} }

%0 = inproceedings %A = Feldman, R. and Dagan, I. %B = Proceedings of the 1st international conference on Knowledge Discovery
and Data mining (KDD'95) %D = 1995 %I = AAAI Press %T = Knowledge discovery in textual databases

@book{Breiman84, author = {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.}, title = {Classification and regression trees}, publisher = {Wadsworth Publishing Company}, year = {1984}, keywords = {FCA, OntologyHandbook} }

%0 = book %A = Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J. %D = 1984 %I = Wadsworth Publishing Company %T = Classification and regression trees

@book{brugger76philosophisches, author = {Brugger, W.}, title = {Philosophisches W�rterbuch}, publisher = {Herder}, address = {Freiburg}, year = {1976}, keywords = {FCA, OntologyHandbook, Philosophie, philosophy} }

%0 = book %A = Brugger, W. %C = Freiburg %D = 1976 %I = Herder %T = Philosophisches W�rterbuch