@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 } @inproceedings{middleton02, abstract = {Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.}, author = {Middleton, Stuart E. and Alani, Harith and Roure, David C. De}, booktitle = {Proceedings of the WWW2002 International Workshop on the Semantic Web}, interhash = {a098783b2b8f386218c3312ebcfa6286}, intrahash = {401e667028f6a4674bb5403ec680d7f3}, note = {cite arxiv:cs.LG/0204012 Comment: Semantic web conference, WWW2002, 10 pages}, title = {Exploiting Synergy Between Ontologies and Recommender Systems}, url = {http://arxiv.org/abs/cs/0204012}, year = 2002 } @inproceedings{middleton01, abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.}, address = {New York, NY, USA}, author = {Middleton, Stuart E. and Roure, David C. De and Shadbolt, Nigel R.}, booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture}, doi = {http://doi.acm.org/10.1145/500737.500755}, interhash = {332dfc15a8f0fc442b47a9a4b740b1bf}, intrahash = {6d0a7792db2c0f96bd0a495a56e57464}, isbn = {1-58113-380-4}, location = {Victoria, British Columbia, Canada}, pages = {100--107}, publisher = {ACM}, title = {Capturing knowledge of user preferences: ontologies in recommender systems}, url = {http://portal.acm.org/citation.cfm?id=500737.500755}, year = 2001 }