Doerfel, S. & Jäschke, R.
(2013):
An analysis of tag-recommender evaluation procedures.
In: Proceedings of the 7th ACM conference on Recommender systems,
New York, NY, USA.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]

Since the rise of collaborative tagging systems on the web, the tag recommendation task - suggesting suitable tags to users of such systems while they add resources to their collection - has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.

@inproceedings{doerfel2013analysis,
author = {Doerfel, Stephan and Jäschke, Robert},
title = {An analysis of tag-recommender evaluation procedures},
booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
series = {RecSys '13},
publisher = {ACM},
address = {New York, NY, USA},
year = {2013},
pages = {343--346},
url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
doi = {10.1145/2507157.2507222},
isbn = {978-1-4503-2409-0},
keywords = {2013, bibsonomy, bookmarking, collaborative, core, evaluation, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging},
abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.}
}

%0 = inproceedings
%A = Doerfel, Stephan and Jäschke, Robert
%B = Proceedings of the 7th ACM conference on Recommender systems
%C = New York, NY, USA
%D = 2013
%I = ACM
%T = An analysis of tag-recommender evaluation procedures
%U = https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf

Heidtmann, K.
(2013):
Internet-Graphen.
In: Informatik-Spektrum,
Ausgabe/Number: 5,
Vol. 36,
Verlag/Publisher: Springer Berlin Heidelberg.
Erscheinungsjahr/Year: 2013.
Seiten/Pages: 440-448.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]

Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale Online-Netze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet.

@article{noKey,
author = {Heidtmann, Klaus},
title = {Internet-Graphen},
journal = {Informatik-Spektrum},
publisher = {Springer Berlin Heidelberg},
year = {2013},
volume = {36},
number = {5},
pages = {440-448},
url = {http://dx.doi.org/10.1007/s00287-012-0654-z},
doi = {10.1007/s00287-012-0654-z},
issn = {0170-6012},
keywords = {Graph, Graphen, Informatik, Informatik-Spektrum, Internet, Spektrum, graphs},
abstract = {Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale Online-Netze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet. }
}

%0 = article
%A = Heidtmann, Klaus
%D = 2013
%I = Springer Berlin Heidelberg
%T = Internet-Graphen
%U = http://dx.doi.org/10.1007/s00287-012-0654-z

Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N.
(2013):
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations.
In: cs.IR,
Vol. 1310.1498,
Erscheinungsjahr/Year: 2013.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

@article{landia2013deeper,
author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
journal = {cs.IR},
year = {2013},
volume = {1310.1498},
url = {http://arxiv.org/abs/1310.1498},
keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging},
abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}
}

%0 = article
%A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan
%D = 2013
%T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations
%U = http://arxiv.org/abs/1310.1498

cite arxiv:0906.2212

: Ghosh, R. & Lerman, K. (2009):*Structure of Heterogeneous Networks*.

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

: Ghosh, R. & Lerman, K. (2009):

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Heterogeneous networks play a key role in the evolution of communities and

e decisions individuals make. These networks link different types of

tities, for example, people and the events they attend. Network analysis

gorithms usually project such networks unto simple graphs composed of

tities of a single type. In the process, they conflate relations between

tities of different types and loose important structural information. We

velop a mathematical framework that can be used to compactly represent and

alyze heterogeneous networks that combine multiple entity and link types. We

neralize Bonacich centrality, which measures connectivity between nodes by

e number of paths between them, to heterogeneous networks and use this

asure to study network structure. Specifically, we extend the popular

dularity-maximization method for community detection to use this centrality

tric. We also rank nodes based on their connectivity to other nodes. One

vantage of this centrality metric is that it has a tunable parameter we can

e to set the length scale of interactions. By studying how rankings change

th this parameter allows us to identify important nodes in the network. We

ply the proposed method to analyze the structure of several heterogeneous

tworks. We show that exploiting additional sources of evidence corresponding

links between, as well as among, different entity types yields new insights

to network structure.

e decisions individuals make. These networks link different types of

tities, for example, people and the events they attend. Network analysis

gorithms usually project such networks unto simple graphs composed of

tities of a single type. In the process, they conflate relations between

tities of different types and loose important structural information. We

velop a mathematical framework that can be used to compactly represent and

alyze heterogeneous networks that combine multiple entity and link types. We

neralize Bonacich centrality, which measures connectivity between nodes by

e number of paths between them, to heterogeneous networks and use this

asure to study network structure. Specifically, we extend the popular

dularity-maximization method for community detection to use this centrality

tric. We also rank nodes based on their connectivity to other nodes. One

vantage of this centrality metric is that it has a tunable parameter we can

e to set the length scale of interactions. By studying how rankings change

th this parameter allows us to identify important nodes in the network. We

ply the proposed method to analyze the structure of several heterogeneous

tworks. We show that exploiting additional sources of evidence corresponding

links between, as well as among, different entity types yields new insights

to network structure.

@misc{Ghosh2009,
author = {Ghosh, Rumi and Lerman, Kristina},
title = {Structure of Heterogeneous Networks},
year = {2009},
note = {cite arxiv:0906.2212

}, url = {http://arxiv.org/abs/0906.2212}, keywords = {graph, graphs, heterogenous, measures, multi-mode, networks, sna}, abstract = { Heterogeneous networks play a key role in the evolution of communities and

e decisions individuals make. These networks link different types of

tities, for example, people and the events they attend. Network analysis

gorithms usually project such networks unto simple graphs composed of

tities of a single type. In the process, they conflate relations between

tities of different types and loose important structural information. We

velop a mathematical framework that can be used to compactly represent and

alyze heterogeneous networks that combine multiple entity and link types. We

neralize Bonacich centrality, which measures connectivity between nodes by

e number of paths between them, to heterogeneous networks and use this

asure to study network structure. Specifically, we extend the popular

dularity-maximization method for community detection to use this centrality

tric. We also rank nodes based on their connectivity to other nodes. One

vantage of this centrality metric is that it has a tunable parameter we can

e to set the length scale of interactions. By studying how rankings change

th this parameter allows us to identify important nodes in the network. We

ply the proposed method to analyze the structure of several heterogeneous

tworks. We show that exploiting additional sources of evidence corresponding

links between, as well as among, different entity types yields new insights

to network structure.

} }

}, url = {http://arxiv.org/abs/0906.2212}, keywords = {graph, graphs, heterogenous, measures, multi-mode, networks, sna}, abstract = { Heterogeneous networks play a key role in the evolution of communities and

e decisions individuals make. These networks link different types of

tities, for example, people and the events they attend. Network analysis

gorithms usually project such networks unto simple graphs composed of

tities of a single type. In the process, they conflate relations between

tities of different types and loose important structural information. We

velop a mathematical framework that can be used to compactly represent and

alyze heterogeneous networks that combine multiple entity and link types. We

neralize Bonacich centrality, which measures connectivity between nodes by

e number of paths between them, to heterogeneous networks and use this

asure to study network structure. Specifically, we extend the popular

dularity-maximization method for community detection to use this centrality

tric. We also rank nodes based on their connectivity to other nodes. One

vantage of this centrality metric is that it has a tunable parameter we can

e to set the length scale of interactions. By studying how rankings change

th this parameter allows us to identify important nodes in the network. We

ply the proposed method to analyze the structure of several heterogeneous

tworks. We show that exploiting additional sources of evidence corresponding

links between, as well as among, different entity types yields new insights

to network structure.

} }

%0 = misc
%A = Ghosh, Rumi and Lerman, Kristina
%D = 2009
%T = Structure of Heterogeneous Networks
%U = http://arxiv.org/abs/0906.2212

Noack, A.
(2008):
*Modularity clustering is force-directed layout*.

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.

@misc{noack08modularity,
author = {Noack, Andreas},
title = {Modularity clustering is force-directed layout},
year = {2008},
url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052},
keywords = {clustering, communities, community, graph, layout, modularity, network, sna},
abstract = { Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.}
}

%0 = misc
%A = Noack, Andreas
%D = 2008
%T = Modularity clustering is force-directed layout
%U = http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052

Zhu, F.; Chen, C.; Yan, X.; Han, J. & Yu, P. S.
(2008):
Graph OLAP: Towards Online Analytical Processing on Graphs.
In: Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.,
[BibTeX][Endnote]

@inproceedings{zhu2008graph,
author = {Zhu, Feida and Chen, Chen and Yan, Xifeng and Han, Jiawei and Yu, Philip S},
title = {Graph OLAP: Towards Online Analytical Processing on Graphs},
booktitle = {Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.},
year = {2008},
keywords = {graph, graphs, olap, sna}
}

%0 = inproceedings
%A = Zhu, Feida and Chen, Chen and Yan, Xifeng and Han, Jiawei and Yu, Philip S
%B = Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.
%D = 2008
%T = Graph OLAP: Towards Online Analytical Processing on Graphs

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.
(2006):
Information Retrieval in Folksonomies: Search and Ranking.
In: Proceedings of the 3rd European Semantic Web Conference,
[BibTeX][Endnote]

@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

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.
(2006):
Content Aggregation on Knowledge Bases using Graph Clustering.
In: The Semantic Web: Research and Applications,
Heidelberg.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]

Recently, research projects such as PADLR and SWAP

have developed tools like Edutella or Bibster, which are targeted at

establishing peer-to-peer knowledge management (P2PKM) systems. In

such a system, it is necessary to obtain provide brief semantic

descriptions of peers, so that routing algorithms or matchmaking

processes can make decisions about which communities peers should

belong to, or to which peers a given query should be forwarded.

have developed tools like Edutella or Bibster, which are targeted at

establishing peer-to-peer knowledge management (P2PKM) systems. In

such a system, it is necessary to obtain provide brief semantic

descriptions of peers, so that routing algorithms or matchmaking

processes can make decisions about which communities peers should

belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on

knowledge bases for that purpose. Using this clustering, we can show

that our strategy requires up to 58% fewer queries than the

baselines to yield full recall in a bibliographic P2PKM scenario.

@inproceedings{schmitz2006content,
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
editor = {Sure, York and Domingue, John},
booktitle = {The Semantic Web: Research and Applications},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2006},
volume = {4011},
pages = {530-544},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
keywords = {2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory},
abstract = {Recently, research projects such as PADLR and SWAP

have developed tools like Edutella or Bibster, which are targeted at

establishing peer-to-peer knowledge management (P2PKM) systems. In

such a system, it is necessary to obtain provide brief semantic

descriptions of peers, so that routing algorithms or matchmaking

processes can make decisions about which communities peers should

belong to, or to which peers a given query should be forwarded.

have developed tools like Edutella or Bibster, which are targeted at

establishing peer-to-peer knowledge management (P2PKM) systems. In

such a system, it is necessary to obtain provide brief semantic

descriptions of peers, so that routing algorithms or matchmaking

processes can make decisions about which communities peers should

belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on

knowledge bases for that purpose. Using this clustering, we can show

that our strategy requires up to 58% fewer queries than the

baselines to yield full recall in a bibliographic P2PKM scenario.}
}

%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = The Semantic Web: Research and Applications
%C = Heidelberg
%D = 2006
%I = Springer
%T = Content Aggregation on Knowledge Bases using Graph Clustering
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf

Brandes, U. & Willhalm, T.
(2002):
Visualization of bibliographic networks with a reshaped landscape metaphor.
In: Proceedings of the symposium on Data Visualisation 2002,
Aire-la-Ville, Switzerland, Switzerland.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]

We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.

@inproceedings{Brandes:2002:VBN:509740.509765,
author = {Brandes, U. and Willhalm, T.},
title = {Visualization of bibliographic networks with a reshaped landscape metaphor},
booktitle = {Proceedings of the symposium on Data Visualisation 2002},
series = {VISSYM '02},
publisher = {Eurographics Association},
address = {Aire-la-Ville, Switzerland, Switzerland},
year = {2002},
pages = {159--ff},
url = {http://portal.acm.org/citation.cfm?id=509740.509765},
isbn = {1-58113-536-X},
keywords = {bibliographic, bibliography, citation, graph, networks, sna},
abstract = {We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.}
}

%0 = inproceedings
%A = Brandes, U. and Willhalm, T.
%B = Proceedings of the symposium on Data Visualisation 2002
%C = Aire-la-Ville, Switzerland, Switzerland
%D = 2002
%I = Eurographics Association
%T = Visualization of bibliographic networks with a reshaped landscape metaphor
%U = http://portal.acm.org/citation.cfm?id=509740.509765

Eklund, P.; Groh, B.; Stumme, G. & Wille, R.
(2000):
Contextual-Logic Extension of TOSCANA..
In: Conceptual Structures: Logical, Linguistic, and Computational,
Heidelberg.
[Volltext]
[BibTeX][Endnote]

@inproceedings{eklund00contextual,
author = {Eklund, P. and Groh, B. and Stumme, G. and Wille, R.},
title = {Contextual-Logic Extension of TOSCANA.},
editor = {Ganter, B. and Mineau, G. W.},
booktitle = {Conceptual Structures: Logical, Linguistic, and Computational},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2000},
volume = {1867},
pages = {453-467},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf},
keywords = {2000, analysis, cg, cgs, concept, conceptual, fca, formal, graph, graphs, iccs, myown, toscana}
}

%0 = inproceedings
%A = Eklund, P. and Groh, B. and Stumme, G. and Wille, R.
%B = Conceptual Structures: Logical, Linguistic, and Computational
%C = Heidelberg
%D = 2000
%I = Springer
%T = Contextual-Logic Extension of TOSCANA.
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf

Prediger, S. & Wille, R.
(1999):
The Lattice of Concept Graphs of a Relationally Scaled Context.
In: ICCS,
[Volltext]
[BibTeX][Endnote]

@inproceedings{prediger99lattice,
author = {Prediger, Susanne and Wille, Rudolf},
title = {The Lattice of Concept Graphs of a Relationally Scaled Context},
editor = {Tepfenhart, William M. and Cyre, Walling R.},
booktitle = {ICCS},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {1999},
volume = {1640},
pages = {401-414},
url = {http://dblp.uni-trier.de/db/conf/iccs/iccs99.html#PredigerW99},
isbn = {3-540-66223-5},
keywords = {analysis, cg, concept, fca, formal, graph, graphs}
}

%0 = inproceedings
%A = Prediger, Susanne and Wille, Rudolf
%B = ICCS
%D = 1999
%I = Springer
%T = The Lattice of Concept Graphs of a Relationally Scaled Context
%U = http://dblp.uni-trier.de/db/conf/iccs/iccs99.html#PredigerW99

Wille, R.
(1997):
Conceptual Graphs and Formal Concept Analysis.
In: Conceptual Structures: Fulfilling Peirce's Dream,
Heidelberg.
[BibTeX][Endnote]

@inproceedings{wille97conceptual,
author = {Wille, Rudolf},
title = {Conceptual Graphs and Formal Concept Analysis},
editor = {Lukose, D. and Delugach, H. and Keeler, M. and Searle, L. and Sowa, J. F.},
booktitle = {Conceptual Structures: Fulfilling Peirce's Dream},
series = {Lecture Notes in Artificial Intelligence},
publisher = {Springer},
address = {Heidelberg},
year = {1997},
volume = {1257},
pages = {290--303},
keywords = {ag1, analysis, begriffsanalyse, cg, concept, conceptual, darmstadt, fba, fca, formal, graph, graphs}
}

%0 = inproceedings
%A = Wille, Rudolf
%B = Conceptual Structures: Fulfilling Peirce's Dream
%C = Heidelberg
%D = 1997
%I = Springer
%T = Conceptual Graphs and Formal Concept Analysis

Stumme, G. & Wille, R.
(1995):
A Geometrical Heuristic for Drawing Concept Lattices.
In: Graph Drawing,
Heidelberg.
[Volltext]
[BibTeX][Endnote]

@inproceedings{stumme95geometrical,
author = {Stumme, Gerd and Wille, Rudolf},
title = {A Geometrical Heuristic for Drawing Concept Lattices},
editor = {Tamassia, R. and Tollis, I.G.},
booktitle = {Graph Drawing},
series = {LNCS},
publisher = {Springer},
address = {Heidelberg},
year = {1995},
volume = {894},
pages = {452-459},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf},
keywords = {1995, analysis, concept, drawing, fca, formal, graph, lattices, myown}
}

%0 = inproceedings
%A = Stumme, Gerd and Wille, Rudolf
%B = Graph Drawing
%C = Heidelberg
%D = 1995
%I = Springer
%T = A Geometrical Heuristic for Drawing Concept Lattices
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf