@inproceedings{schmitz2006content, 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. 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.}, address = {Heidelberg}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {d2ddbb8f90cd271dc18670e4c940ccfb}, intrahash = {1788c88e04112a4491f19dfffb8dc39e}, pages = {530-544}, publisher = {Springer}, series = {LNAI}, title = {Content Aggregation on Knowledge Bases using Graph Clustering}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf}, volume = 4011, year = 2006 } @incollection{hoser2006semantic, abstract = { A key argument for modeling knowledge in ontologies is the easy reuse and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA).While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size. }, address = {Berlin/Heidelberg}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, doi = {10.1007/11762256_38}, editor = {Sure, York and Domingue, John}, interhash = {344ec3b4ee8af1a2c6b86efc14917fa9}, intrahash = {2b720233e4493d4e0dee95be86dd07e8}, isbn = {978-3-540-34544-2}, note = {10.1007/11762256_38}, pages = {514--529}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Semantic Network Analysis of Ontologies}, url = {http://dx.doi.org/10.1007/11762256_38}, volume = 4011, year = 2006 } @inproceedings{haase2005collaborative, abstract = {Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.}, author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York}, booktitle = {ESWC}, crossref = {conf/esws/2005}, date = {2005-05-24}, editor = {Gómez-Pérez, Asunción and Euzenat, Jérôme}, ee = {http://dx.doi.org/10.1007/11431053_33}, file = {haase2005collaborative.pdf:haase2005collaborative.pdf:PDF}, groups = {public}, interhash = {c9ba81293a1b27f1c9bdf38a3beec060}, intrahash = {1a8829cde1cb26241a48901e28a953d2}, isbn = {3-540-26124-9}, pages = {486-499}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2009-11-10 11:30:42}, title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.}, url = {http://www.aifb.uni-karlsruhe.de/WBS/pha/publications/collaborative05eswc.pdf}, username = {dbenz}, volume = 3532, year = 2005 } @inproceedings{benz2010semantics, address = {Raleigh, NC, USA}, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, interhash = {dbd2ac30cfb0faa29413275afc9b4387}, intrahash = {ba43b0db4b8f7cb091fd55d59e170477}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, year = 2010 } @inproceedings{schmitz2006mining, address = {Berlin, Heidelberg}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Data Science and Classification: Proc. of the 10th IFCS Conf.}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and {\v Z}iberna, A.}, interhash = {20650d852ca3b82523fcd8b63e7c12d7}, intrahash = {1e79a0f1c79561073d14434adce1e890}, pages = {261--270}, publisher = {Springer}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, title = {Mining Association Rules in Folksonomies}, year = 2006 } @inproceedings{hoser2006semantic, abstract = {A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, interhash = {344ec3b4ee8af1a2c6b86efc14917fa9}, intrahash = {9a2c77c7c7a1b19cd16df08cca65f706}, month = {June}, note = {Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Semantic Network Analysis of Ontologies}, year = 2006 } @inproceedings{hoser2006semantic, abstract = {A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.}, address = {Heidelberg}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {344ec3b4ee8af1a2c6b86efc14917fa9}, intrahash = {c0cdbeab23ce0fc1bff5e02c99aab012}, month = {June}, pages = {514-529}, publisher = {Springer}, series = {LNAI}, title = {Semantic Network Analysis of Ontologies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf}, volume = 4011, year = 2006 } @inproceedings{haase2005collaborative, abstract = {Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.}, address = {Berlin/Heidelberg}, author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York}, booktitle = {The Semantic Web: Research and Applications}, doi = {10.1007/11431053_33}, editor = {Gómez-Pérez, Asuncion and Euzenat, Jerome}, interhash = {c9ba81293a1b27f1c9bdf38a3beec060}, intrahash = {258348df63fd814cb7e4ccc9762f9d8c}, isbn = {3-540-26124-9}, pages = {486--499}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.}, url = {http://dx.doi.org/10.1007/11431053_33}, volume = 3532, year = 2005 } @inproceedings{cattuto2008semantic, abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, address = {Patras, Greece}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)}, interhash = {cc62b733f6e0402db966d6dbf1b7711f}, intrahash = {3b0aca61b24e4343bd80390614e3066e}, isbn = {978-960-89282-6-8}, month = jul, pages = {39--43}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, url = {http://olp.dfki.de/olp3/}, year = 2008 } @inproceedings{benz07ontology, abstract = {The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting "folksonomies". An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure. }, address = {Halle/Saale}, author = {Benz, Dominik and Hotho, Andreas}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {ff7de5717f771dabd764675279ff3adf}, intrahash = {72bff5ebe5dfb5023f62ba9b94e6ed01}, isbn = {978-3-86010-907-6}, month = sep, pages = {109--112}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Position Paper: Ontology Learning from Folksonomies}, url = {http://lwa07.informatik.uni-halle.de/kdml07/kdml07.htm}, year = 2007 } @article{voelker2008aeon, abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas}, interhash = {f14794f4961d0127dc50c1938eaef7ea}, intrahash = {f8f0bb3e3495e7627770b470d1a5f1a3}, issn = {1570-5838}, journal = {Applied Ontology}, number = {1-2}, pages = {41--62}, publisher = {IOS Press}, title = {AEON - An approach to the automatic evaluation of ontologies}, url = {http://portal.acm.org/citation.cfm?id=1412422}, volume = 3, year = 2008 } @article{voelker2008aeon, abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas}, interhash = {f14794f4961d0127dc50c1938eaef7ea}, intrahash = {f8f0bb3e3495e7627770b470d1a5f1a3}, issn = {1570-5838}, journal = {Applied Ontology}, number = {1-2}, pages = {41--62}, publisher = {IOS Press}, title = {AEON - An approach to the automatic evaluation of ontologies}, url = {http://portal.acm.org/citation.cfm?id=1412422}, volume = 3, year = 2008 } @misc{cattuto-2008, abstract = { Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, interhash = {cc62b733f6e0402db966d6dbf1b7711f}, intrahash = {78fd64c3db55e6387ebdeb6c40054542}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045}, year = 2008 } @inproceedings{Benz07OL, author = {Benz, Dominik and Hotho, Andreas}, booktitle = {LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)}, crossref = {conf/lwa/2007}, date = {2007-11-16}, editor = {Hinneburg, Alexander}, interhash = {ff7de5717f771dabd764675279ff3adf}, intrahash = {ad31989b2393f5d0c4e8be8dbb613141}, isbn = {978-3-86010-907-6}, pages = {109-112}, publisher = {Martin-Luther-University Halle-Wittenberg}, title = {Position Paper: Ontology Learning from Folksonomies.}, url = {http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07}, vgwort = {16}, year = 2007 } @inproceedings{voelker1:07:eswc, author = {Völker, Johanna and Vrandecic, Denny and Sure, York and Hotho, Andreas}, booktitle = {Proceedings of the European Semantic Web Conference, ESWC2007}, editor = {Franconi, Enrico and Kifer, Michael and May, Wolfgang}, interhash = {5a5b17f5657ccff6fa7fd17dae4ae503}, intrahash = {c5c43ae4a719e6e935a9ca1a4aca906b}, month = {July}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, title = {{Learning Disjointness}}, url = {http://www.eswc2007.org/pdf/eswc07-voelker1.pdf}, vgwort = {26}, volume = 4519, year = 2007 } @inproceedings{658040, address = {Washington, DC, USA}, author = {Hotho, Andreas and Maedche, Alexander and Staab, Steffen}, booktitle = {ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining}, interhash = {e2f356aeefc84fd73c9bcdc08392edf0}, intrahash = {a6803e87c5145d5f55d7bb1bab8dfd67}, isbn = {0-7695-1119-8}, pages = {607--608}, publisher = {IEEE Computer Society}, title = {Text Clustering Based on Good Aggregations}, url = {http://portal.acm.org/citation.cfm?id=658040}, year = 2001 } @inproceedings{schmitz2006content, address = {Budva, Montenegro}, author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, interhash = {940fa3c671c771cc9a644b3ecfef43cd}, intrahash = {9a06428ec3bd72e3ea6c7a8f08e2bb85}, isbn = {3-540-34544-2}, month = {June}, pages = {530-544}, publisher = {Springer}, series = {LNCS}, title = {Content Aggregation on Knowledge Bases using Graph Clustering}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf}, vgwort = {27}, volume = 4011, year = 2006 } @inproceedings{hoser2006semantic, address = {Budva, Montenegro}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, interhash = {344ec3b4ee8af1a2c6b86efc14917fa9}, intrahash = {85f032bf2ffe9b1b55f8656c8a0d6d70}, isbn = {3-540-34544-2}, month = {June}, pages = {514-529}, publisher = {Springer}, series = {LNCS}, title = {Semantic Network Analysis of Ontologies}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/hoser_sna_eswc2005.pdf}, vgwort = {29}, volume = 4011, year = 2006 } @inproceedings{cim04b, author = {Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien}, booktitle = {Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04)}, interhash = {e42d9895b0d816f231227f1be15b03dc}, intrahash = {ef6665b5a80e7eacd31a18f36408f9e6}, isbn = {3-540-21043-1}, publisher = {Springer}, series = {LNCS}, title = {Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/icfca04.pdf}, volume = 2961, year = 2004 } @inproceedings{cim04a, address = {Lisbon, Portugal}, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, booktitle = {Proceedings of the Conference on Languages Resources and Evaluation (LREC)}, interhash = {9374d126c328dab48f52854f73d6db4f}, intrahash = {3bc6e5a51dba862da1b7b3b6ac563370}, month = MAY, publisher = {ELRA - European Language Ressources Association}, title = {Clustering Ontologies from Text}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf}, year = 2004 }