@article{Gronau:jucs_11_4:kmdl_capturing_analysing_and,
        title = {KMDL - Capturing, Analysing and Improving Knowledge-Intensive Business Processes},
        author = {N. Gronau and C. Müller and R. Korf},
        journal = {Journal of Universal Computer Science},
        note = {\url|http://www.jucs.org/jucs_11_4/kmdl_capturing_analysing_and|},
        number = 4,
        pages = {452--472},
        volume = 11,
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/299c64d30eb2f5e0a421d979a89aaad2e/schmitz},
	keywords = {knowledgemanagement modeling kmdl}
}

@inproceedings{motik2002conceptual,
        title = {A Conceptual Modeling Approach for building
                  semantics-driven enterprise applications},
        author = {Boris Motik and Alexander Maedche and Raphael Volz},
        booktitle = {Proc.\ First International Conference on Ontologies,
                  Databases and Application of Semantics
                  (ODBASE-2002)},
        month = {October},
        year = 2002,
        misc = {isbn = {1-58133-109-7},
	biburl = {http://www.bibsonomy.org/bibtex/25916065adaf6abd9b01c006ff526641b/schmitz},
	keywords = {ontology modeling semanticweb}
}

@incollection{citeulike:383010,
        title = {Probabilistic topic models},
        author = {M. Steyvers and T. Griffiths},
        booktitle = {Latent Semantic Analysis: A Road to Meaning},
        editor = {T. Landauer and D. Mcnamara and S. Dennis and W. Kintsch},
        publisher = {Laurence Erlbaum},
        year = 2005,
        url = {http://psiexp.ss.uci.edu/research/papers/SteyversGriffithsLSABookFormatted.pdf},
        id = {383010},
	priority = {0},
	abstract = {Many chapters in this book illustrate that applying a statistical method such as Latent Semantic Analysis (LSA;
Landauer \& Dumais, 1997; Landauer, Foltz, \& Laham, 1998) to large databases can yield insight into human
cognition. The LSA approach makes three claims: that semantic information can be derived from a word-document
co-occurrence matrix; that dimensionality reduction is an essential part of this derivation; and that words and
documents can be represented as points in Euclidean space. In this chapter, we pursue an approach that is consistent
with the first two of these claims, but differs in the third, describing a class of statistical models in which the
semantic properties of words and documents are expressed in terms of probabilistic topics.},
	biburl = {http://www.bibsonomy.org/bibtex/216802fb465ca95ac77434bf73c6b271b/schmitz},
	keywords = {modeling generative statistics}
}

@inproceedings{citeulike:391307,
        title = {The author-topic model for authors and documents},
        address = {Arlington, VA, USA},
        author = {Michal Rosen-Zvi and Thomas Griffiths and Mark Steyvers and Padhraic Smyth},
        booktitle = {Proceedings of the 20th conference on Uncertainty in artificial intelligence},
        pages = {487--494},
        publisher = {AUAI Press},
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=1036843.1036902},
        id = {391307},
	priority = {0},
	isbn = {0974903906},
	comment = {ATM cite this},
	biburl = {http://www.bibsonomy.org/bibtex/2a4dd688efe5778fb99ff94de104211aa/schmitz},
	keywords = {modeling generative statistics}
}

@inproceedings{citeulike:344452,
        title = {Topic and role discovery in social networks},
        author = {A. Mccallum and A. Corrada-Emmanuel and X. Wang},
        booktitle = {ijcai.org},
        year = 2005,
        url = {http://www.cs.umass.edu/~mccallum/papers/art-ijcai05.pdf},
        id = {344452},
	priority = {0},
	abstract = {Previous work in social network analysis (SNA)
has modeled the existence of links from one entity
to another, but not the language content or
topics on those links. We present the Author-
Recipient-Topic (ART) model for social network
analysis, which learns topic distributions based on
the direction-sensitive messages sent between entities.
The model builds on Latent Dirichlet Allocation
(LDA) and the Author-Topic (AT) model,
adding the key attribute that distribution over topics
is conditioned distinctly on both the sender and
recipient—steering the discovery of topics according
to the relationships between people. We give
results on both the Enron email corpus and a researcher’s
email archive, providing evidence not
only that clearly relevant topics are discovered, but
that the ART model better predicts people’s roles.},
	biburl = {http://www.bibsonomy.org/bibtex/24a5138f8d572d2f89e2b94ec60986278/schmitz},
	keywords = {statistics generative modeling}
}