@book{staab2009handbook, abstract = {An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. This book considers ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications.}, address = {Berlin}, author = {Staab, Steffen and Studer, Rudi}, interhash = {c2e7c401bef2cee2bb8b12334d3c7a88}, intrahash = {be122d99dc6dd20cb58a55d62d8eca6c}, isbn = {9783540926733 3540926739}, publisher = {Springer}, refid = {569892085}, title = {Handbook on ontologies}, url = {http://public.eblib.com/choice/publicfullrecord.aspx?p=571805}, year = 2009 } @article{cimiano05learning, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, ee = {http://www.jair.org/papers/paper1648.html}, interhash = {4c09568cff62babd362aab03095f4589}, intrahash = {eaaf0e4b3a8b29fab23b6c15ce2d308d}, journal = {Journal on Artificial Intelligence Research}, pages = {305-339}, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, url = {http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05}, volume = 24, year = 2005 } @inproceedings{conf/birthday/BloehdornBCGHLMMSSV11, author = {Bloehdorn, Stephan and Blohm, Sebastian and Cimiano, Philipp and Giesbrecht, Eugenie and Hotho, Andreas and Lösch, Uta and Mädche, Alexander and Mönch, Eddie and Sorg, Philipp and Staab, Steffen and Völker, Johanna}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, ee = {http://dx.doi.org/10.1007/978-3-642-19797-0_7}, interhash = {db48314326a36fc4ac8770cba2c20e49}, intrahash = {21be5153a8f491c9f209d57ce7662387}, isbn = {978-3-642-19796-3}, pages = {115-142}, publisher = {Springer}, title = {Combining Data-Driven and Semantic Approaches for Text Mining.}, url = {http://dblp.uni-trier.de/db/conf/birthday/studer2011.html#BloehdornBCGHLMMSSV11}, year = 2011 } @incollection{cimiano2009ontology, affiliation = {University of Karlsruhe Institute AIFB Karlsruhe Germany}, author = {Cimiano, Philipp and M{\"{a}}dche, Alexander and Staab, Steffen and V{\"{o}}lker, Johanna}, booktitle = {Handbook on Ontologies}, editor = {Staab, Steffen and Studer, Rudi}, interhash = {884c5b59450bf7982a4345f523181404}, intrahash = {3081beee709710cd12ca402a00526ef2}, isbn = {978-3-540-92673-3}, keyword = {Economics/Management Science}, pages = {245-267}, publisher = {Springer Berlin Heidelberg}, series = {International Handbooks Information System}, title = {Ontology Learning}, url = {http://dx.doi.org/10.1007/978-3-540-92673-3_11}, year = 2009 } @incollection{guarino2009ontology, affiliation = {ITSC-CNR, Laboratory for Applied Ontology, 38100 Trento, Italy}, author = {Guarino, Nicola and Oberle, Daniel and Staab, Steffen}, booktitle = {Handbook on Ontologies}, editor = {Staab, Steffen and Studer, Dr. Rudi and Bernus, Peter and Błażewicz, Jacek and Schmidt, Günter J. and Shaw, Michael J.}, interhash = {c0f4c3b6821595bdc50104f5a9af29a0}, intrahash = {f512ff3d350290e87a23935e90ad1f65}, isbn = {978-3-540-92673-3}, keyword = {Bücher}, note = {10.1007/978-3-540-92673-3_0}, pages = {1-17}, publisher = {Springer Berlin Heidelberg}, series = {International Handbooks on Information Systems}, title = {What Is an Ontology?}, url = {http://dx.doi.org/10.1007/978-3-540-92673-3_0}, year = 2009 } @inproceedings{dellschaft2008epistemic, abstract = {In recent literature, several models were proposed for reproducing and understanding the tagging behavior of users. They all assume that the tagging behavior is influenced by the previous tag assignments of other users. But they are only partially successful in reproducing characteristic properties found in tag streams. We argue that this inadequacy of existing models results from their inability to include user's background knowledge into their model of tagging behavior. This paper presents a generative tagging model that integrates both components, the background knowledge and the influence of previous tag assignments. Our model successfully reproduces characteristic properties of tag streams. It even explains effects of the user interface on the tag stream.}, acmid = {1379109}, address = {New York, NY, USA}, author = {Dellschaft, Klaas and Staab, Steffen}, booktitle = {Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, doi = {10.1145/1379092.1379109}, interhash = {cc0d1d4f43effbb6eb7d463422e6c00b}, intrahash = {7877bf1d91bd35067461c306b7f6fd00}, isbn = {978-1-59593-985-2}, location = {Pittsburgh, PA, USA}, numpages = {10}, pages = {71--80}, publisher = {ACM}, series = {HT '08}, title = {An epistemic dynamic model for tagging systems}, url = {http://doi.acm.org/10.1145/1379092.1379109}, year = 2008 } @inproceedings{abbasi2009richvsm, author = {Abbasi, Rabeeh and Staab, Steffen}, booktitle = {HyperText'09: Proceedings of 20th ACM conference on Hypertext and Hypermedia}, interhash = {beeda6b9f798af218a7f51aaa399e45e}, intrahash = {741225081d2cfcecec84b5cc2807fdb2}, location = {Torino, Italy}, title = {{RichVSM: enRiched Vector Space Models for Folksonomies}}, year = 2009 } @inproceedings{aberer2004emergent, author = {Aberer, Karl and Cudr\'e-Mauroux, Philippe and Ouksel, Aris M. and Catarci, Tiziana and Hacid, Mohand-Said and Illarramendi, Arantza and Kashyap, Vipul and Mecella, Massimo and Mena, Eduardo and Neuhold, Erich J. and Troyer, Olga De and Risse, Thomas and Scannapieco, Monica and Saltor, F\`elix and Santis, Luca De and Spaccapietra, Stefano and Staab, Steffen and Studer, Rudi}, booktitle = {Proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA'04)}, editor = {Lee, Yoon-Joon and Li, Jianzhong and Whang, Kyu-Young and Lee, Doheon}, interhash = {4a644b67e30bfb1c342413b139b46270}, intrahash = {3dd280a5313df86cd5747e5bc91bc5c6}, isbn = {3-540-21047-4}, pages = {25-38}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Emergent Semantics Principles and Issues.}, volume = 2973, year = 2004 } @inproceedings{bozsak2002towards, author = {Bozsak, E. and Ehrig, Marc and Handschuh, Siegfried and Hotho, Andreas and Maedche, Alexander and Motik, Boris and Oberle, Daniel and Schmitz, Christoph and Staab, Steffen and Stojanovic, Ljiljana and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Sure, York and Tane, Julien and Volz, Raphael and Zacharias, Valentin}, booktitle = {Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France}, editor = {Bauknecht, Kurt and Tjoa, A. Min and Quirchmayr, Gerald}, interhash = {940750309ac472ea48a712e16b5d902a}, intrahash = {d0aa1d2d01e378046e1693babc026836}, pages = {304-313}, publisher = {Springer}, series = {LNCS}, title = {KAON - Towards a large scale Semantic Web}, url = {http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2002_ecweb_kaon.pdf}, volume = 2455, year = 2002 } @inproceedings{maedche2002measuring, address = {London, UK}, author = {Maedche, Alexander and Staab, Steffen}, booktitle = {EKAW '02: Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web}, file = {:maedche2002measuring.pdf:PDF}, interhash = {d5b06cd1af41e25a751ab755fb3a0068}, intrahash = {3a3b029259f39e1e1893012f5e8a7b1e}, pages = {251--263}, publisher = {Springer-Verlag}, title = {Measuring Similarity between Ontologies}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.5761&rep=rep1&type=pdf}, year = 2002 } @inproceedings{cimiano2003automaticb, author = {Cimiano, Philipp and Staab, Steffen and Tane, Julien}, booktitle = {Proceedings of the ECML/PKDD Workshop on Adaptive Text Extraction and Mining, Cavtat-Dubrovnik, Croatia}, interhash = {2f9df79fa0d890faa91dc1d0d0def735}, intrahash = {c62b4e1dc65490d68bef7eaed01f83ea}, lastdatemodified = {2007-03-22}, lastname = {Cimiano}, own = {notown}, pages = {10-17}, pdf = {cimiano03-automatic.pdf}, read = {notread}, title = {Automatic Acquisition of Taxonomies from Text: FCA meets NLP}, url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/pci/ontolearning.pdf}}, year = 2003 } @misc{erdmann2000from, abstract = {Semantic Annotation is a basic technology for intelligent content and is beneficial in a wide range of contentoriented intelligent applications. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KA # -initiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation. 1 Introduction The KA # -initiative (Knowledge Annotation initiative of the Knowledge Acquisition community) was launched at EKAW in 1997 in order to provide semantic access to inform...}, author = {Erdmann, Michael and Maedche, Alexander and Schnurr, Hans-Peter and Staab, Steffen}, file = {erdmann2000from.pdf:erdmann2000from.pdf:PDF}, interhash = {57d3bfc9b4d1fb72754c050790f8c3fd}, intrahash = {19bb2756cd3da755a3a5238b01cd4d2b}, lastdatemodified = {2006-07-13}, lastname = {Erdmann}, longnotes = {[[http://citeseer.ist.psu.edu/283251.html citeseer]]}, month = {jun 23}, own = {own}, pdf = {erdmann00-from.pdf}, read = {readnext}, title = {From Manual to Semi-automatic Semantic Annotation: About Ontology-based Text Annotation Tools}, year = 2000 } @inproceedings{cimiano2003automatic, abstract = {We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from domain-specific texts based on Formal Concept Analysis (FCA). Our approach is based on the assumption that verbs pose more or less strong selectional restrictions on their arguments. The conceptual hierarchy is then built on the basis of the inclusion relations between the extensions of the selectional restrictions of all the verbs, while the verbs themselves provide intensional descriptions for each concept. We formalize this idea in terms of FCA and show how our approach can be used to acquire a concept hierarchy for the tourism domain out of texts. We then evaluate our method by considering an already existing ontology for this domain.}, address = {Cavtat-Dubrovnik, Croatia}, author = {Cimiano, Philipp and Staab, Steffen and Tane, Julien}, booktitle = {Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining}, file = {cimiano2003automatic.pdf:cimiano2003automatic.pdf:PDF}, groups = {public}, interhash = {2f9df79fa0d890faa91dc1d0d0def735}, intrahash = {573ac0e71d6b1c369cf881ddda8c7841}, pages = {10--17}, pdf = {cimiano2003automatic.pdf}, timestamp = {2008-07-07 15:50:15}, title = {Automatic Acquisition of Taxonomies from Text: FCA meets NLP}, url = {http://www.dcs.shef.ac.uk/~fabio/ATEM03/cimiano-ecml03-atem.pdf}, username = {dbenz}, year = 2003 } @inproceedings{dellschaft2006how, abstract = {In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that there exist some measures sufficient for evaluating the lexical term layer. However, existing measures for the evaluation of concept hierarchies fail to meet basic criteria. This paper presents a new taxonomic measure which overcomes the problems of current approaches.}, address = {Athens, GA, USA}, author = {Dellschaft, Klaas and Staab, Steffen}, booktitle = {Proceedings of ISWC-2006 International Semantic Web Conference}, file = {dellschaft2006how.pdf:dellschaft2006how.pdf:PDF}, groups = {public}, interhash = {bd5dcdc47711f5dce1a2546db5b66e79}, intrahash = {e4229fc66e502586f7fd4a5bf0ed2957}, lastdatemodified = {2007-04-27}, lastname = {Dellschaft}, link = {http://dx.doi.org/10.1007/11926078_17}, month = {November}, own = {notown}, pdf = {dellschaft06-how.pdf}, publisher = {Springer, LNCS}, read = {notread}, timestamp = {2007-09-11 13:31:25}, title = {On How to Perform a Gold Standard based Evaluation of Ontology Learning}, url = {http://iswc2006.semanticweb.org/submissions/res_ac_track.htm}, username = {dbenz}, year = 2006 } @inproceedings{tane2003courseware, abstract = {Topics in education are changing with an ever faster pace. E-Learningresources tend to be more and more decentralised. Users need increasingly to be able touse the resources of the web. For this, they should have tools for finding and organizinginformation in a decentral way. In this, paper, we show how an ontology-based toolsuite allows to make the most of the resources available on the web.}, author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd and Staab, Steffen and Studer, R.}, booktitle = {Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität}, editor = {David, Klaus and Wegner, Lutz}, file = {tane2003courseware.pdf:tane2003courseware.pdf:PDF}, groups = {public}, interhash = {7f33080bb78d089b24bf51c059f8f018}, intrahash = {850949481723b7dd03768ccd96b25cb9}, month = {November}, pages = {93-104}, privnote = {alpha}, publisher = {Kassel University Press}, timestamp = {2010-11-10 15:35:25}, title = {The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf}, username = {dbenz}, year = 2003 } @inproceedings{cimiano2004comparing, abstract = {The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis for automatic taxonomy construction and compare it with hierarchical agglomerative clustering and hierarchical divisive clustering.}, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, booktitle = {ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, 22 - 27 August, Valencia, Spain}, editor = {de M\'{a}ntaras, R. L\'{o}pez and Saitta, L.}, file = {cimiano2004comparing.pdf:cimiano2004comparing.pdf:PDF}, groups = {public}, interhash = {5ebc73142f0c4d51a1037432435bab94}, intrahash = {4e2f4ba3e051f120c2bc8216aad7cdaa}, pages = {435-439}, publisher = {IOS Press}, timestamp = {2011-02-02 13:38:11}, title = {Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text}, username = {dbenz}, year = 2004 } @inproceedings{Abbasi09, abstract = {People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries.}, address = {New York, NY, USA}, author = {Abbasi, Rabeeh and Staab, Steffen}, booktitle = {HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia}, citeulike-article-id = {5031176}, doi = {10.1145/1557914.1557952}, interhash = {beeda6b9f798af218a7f51aaa399e45e}, intrahash = {fa2c56a067dc00f073518cca3fd5dfae}, isbn = {978-1-60558-486-7}, location = {Torino, Italy}, pages = {219--228}, posted-at = {2009-07-01 09:11:16}, priority = {2}, publisher = {ACM}, title = {RichVSM: enRiched vector space models for folksonomies}, url = {http://dx.doi.org/10.1145/1557914.1557952}, year = 2009 } @techreport{hotho03textclustering, abstract = {Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, comment = {alpha}, institution = {University of Karlsruhe, Institute AIFB}, interhash = {0bc7c3fc1273355f45c8970a7ea58f97}, intrahash = {61d58db419af0dbc3681432588219c3d}, privnote = {alpha}, title = {Text Clustering Based on Background Knowledge}, type = {Technical Report }, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf}, volume = 425, year = 2003 } @inproceedings{hotho03ontologies, address = {Melbourne, Florida}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Proceedings of the 2003 IEEE International Conference on Data Mining}, comment = {alpha}, interhash = {b56c36d6d9c9ca9e6bd236a0f92415a5}, intrahash = {57a39c81cff1982dbefed529be934bee}, month = {November 19-22,}, pages = {541-544 (Poster}, privnote = {alpha}, publisher = {IEEE {C}omputer {S}ociety}, title = {Ontologies improve text document clustering}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf}, year = 2003 } @inproceedings{hotho03explaining, abstract = {Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.}, address = {Heidelberg}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases}, comment = {alpha}, editor = {Lavra\v{c}, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco}, interhash = {cf66183151a5d94a0941ac6d5089ae89}, intrahash = {53a943b6be4b34cf4e5329d0b58e99f6}, pages = {217-228}, privnote = {alpha}, publisher = {Springer}, series = {LNAI}, title = {Explaining Text Clustering Results using Semantic Structures}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf}, volume = 2838, year = 2003 }