Jäschke, R. & Rudolph, S.: Attribute Exploration on the Web. In: Cellier, P.; Distel, F. & Ganter, B. (Hrsg.): Contributions to the 11th International Conference on Formal Concept Analysis. 2013, S. 19-34
[Volltext]
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.

Krause, B.; Jäschke, R.; Hotho, A. & Stumme, G.: Logsonomy - Social Information Retrieval with Logdata. HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia. New York, NY, USA: ACM, 2008, S. 157-166
[Volltext]
Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

Ducrou, J.; Vormbrock, B. & Eklund, P. W.: FCA-based Browsing and Searching of a Collection of Images. Proc. of the 14 th Int. Conference on Conceptual Structures. Springer-Verlag, 2006

FCA-based Browsing and Searching of a Collection of Images. In: Ducrou, J.; Vormbrock, B. & Eklund, P. W. (Hrsg.): Proc. of the 14 th Int. Conference on Conceptual Structures. Springer-Verlag, 2006

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. Proceedings of the 3rd European Semantic Web Conference. Springer, 2006Lecture Notes in Computer Science , S. 411-426

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: Sure, Y. & Domingue, J. (Hrsg.): The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 411-426

Hotho, A.; J�schke, R.; Schmitz, C. & Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: Sure, Y. & Domingue, J. (Hrsg.): The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 411-426

Cole, R. J.; Eklund, P. W. & Stumme, G.: Document Retrieval for Email Search and Discovery using Formal Concept Analysis. In: Journal of Applied Artificial Intelligence (AAI) 17 (2003), Nr. 3, S. 257-280
[Volltext]
This paper discusses an document discovery tool based on
nceptual clustering by formal concept analysis. The program
lows users to navigate email using a visual lattice metaphor
ther than a tree. It implements a virtual file structure over
ail where files and entire directories can appear in multiple
sitions. The content and shape of the lattice formed by the
nceptual ontology can assist in email discovery. The system
scribed provides more flexibility in retrieving stored emails
an what is normally available in email clients. The paper
scusses how conceptual ontologies can leverage traditional
cument retrieval systems and aid knowledge discovery in document
llections.

Ferber, R.: Information Retrieval: Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web. Heidelberg: dpunkt Verlag, 2003
[Volltext]

Hotho, A.; Staab, S. & Stumme, G.: Wordnet improves text document clustering. Proc. SIGIR Semantic Web Workshop. Toronto: 2003
[Volltext]

Sarwar, B. M.; Karypis, G.; Konstan, J. A. & Riedl, J.: Item-based collaborative filtering recommendation algorithms.. WWW. 2001, S. 285-295
[Volltext]

Baeza-Yates, R. A. & Ribeiro-Neto, B. A.: Modern Information Retrieval. ACM Press / Addison-Wesley, 1999
[Volltext]

Stumme, G.: Dual Retrieval in Conceptual Information Systems. In: Buchmann, A. (Hrsg.): Datenbanksysteme in Büro, Technik und Wissenschaft. Proc. BTW'99. Heidelberg: 1999, S. 328-342
[Volltext]

Sparck-Jones, K. & Willett, P. (Hrsg.): Readings in Information Retrieval. Morgan Kaufmann, 1997

Carpineto, C. & Romano, G.: A lattice conceptual clustering system and its application to browsing retrieval. In: Machine Learning 24 (1996), Nr. 2, S. 95-122
[Volltext]
The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge.
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Lang, K.: NewsWeeder: learning to filter netnews. Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann publishers Inc.: San Mateo, CA, USA, 1995, S. 331-339
[Volltext]

Voorhees, E. M.: Query Expansion Using Lexical-Semantic Relations.. SIGIR. 1994, S. 61-69
[Volltext]

van Rijsbergen, C. J.: Information retrieval. 2. Aufl. London: Butterworths, 1979
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