@incollection{solskinnsbakk2010hybrid, abstract = {Folksonomies are becoming increasingly popular. They contain large amounts of data which can be mined and utilized for many tasks like visualization, browsing, information retrieval etc. An inherent problem of folksonomies is the lack of structure. In this paper we present an unsupervised approach for generating such structure based on a combination of association rule mining and the underlying tagged material. Using the underlying tagged material we generate a semantic representation of each tag. The semantic representation of the tags is an integral component of the structure generated. The experiment presented in this paper shows promising results with tag structures that correspond well with human judgment.}, address = {Berlin / Heidelberg}, affiliation = {Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway}, author = {Solskinnsbakk, Geir and Gulla, Jon}, booktitle = {On the Move to Meaningful Internet Systems, OTM 2010}, doi = {10.1007/978-3-642-16949-6_22}, editor = {Meersman, Robert and Dillon, Tharam and Herrero, Pilar}, interhash = {c33c0fe08d8ac29e88a4c43b3047c707}, intrahash = {949d497bc5a29eda10c77f5784aed18b}, isbn = {978-3-642-16948-9}, keyword = {Computer Science}, pages = {975-982}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, slides = {http://www.slides.com}, title = {A Hybrid Approach to Constructing Tag Hierarchies}, url = {http://dx.doi.org/10.1007/978-3-642-16949-6_22}, volume = 6427, year = 2010 } @inproceedings{plangprasopchok2010probabilistic, abstract = {Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering. }, author = {Plangprasopchok, Anon and Lerman, Kristina and Getoor, Lise}, booktitle = {Proceedings of the 4th ACM Web Search and Data Mining Conference}, interhash = {826359ec25dcd228ad3ef46dcc6d26c5}, intrahash = {455bb173bb33af58bc8aaed48d8a8513}, note = {cite arxiv:1011.3557Comment: In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)}, title = {A Probabilistic Approach for Learning Folksonomies from Structured Data}, url = {http://arxiv.org/abs/1011.3557}, year = 2010 } @inproceedings{zhou2008unsupervised, abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.}, author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong}, file = {zhou2008unsupervised.pdf:zhou2008unsupervised.pdf:PDF}, groups = {public}, interhash = {e8397fd51d43531b91e81776c879f487}, intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819}, journal = {The Semantic Web}, pages = {680--693}, timestamp = {2009-09-24 23:27:32}, title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations}, url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49}, username = {dbenz}, year = 2008 } @article{lux2008from, abstract = {Is Web 2.0 just hype or just a buzzword, which might disappear in the near future One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 the folksonomy and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.}, author = {Lux, Mathias and Dösinger, Gisela}, doi = {10.1504/IJKL.2007.016709}, groups = {public}, interhash = {5dde7a91231320f96c0c4b3e7ba9a503}, intrahash = {dd5cdcc6449d97622033bbebcd4d1874}, journal = {International Journal of Knowledge and Learning}, journalpub = {1}, month = jan, number = {4-5}, pages = {515--528}, timestamp = {2010-08-11 07:26:38}, title = {From folksonomies to ontologies: employing wisdom of the crowds to serve learning purposes}, url = {http://www.ingentaconnect.com/content/ind/ijkl/2008/00000003/F0020004/art00009}, username = {dbenz}, volume = 3, year = 2008 } @article{zhou2008unsupervised, abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.}, author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong}, file = {zhou2008unsupervised.pdf:zhou2008unsupervised.pdf:PDF}, interhash = {e8397fd51d43531b91e81776c879f487}, intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819}, journal = {The Semantic Web}, pages = {680--693}, title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations}, url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49}, year = 2008 } @inproceedings{marinho2008folksonomybased, abstract = {The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows down the full materialization of the SemanticWeb since these systems allow ordinary users to create and share knowledge in a simple, cheap, and scalable representation, usually known as folksonomy. However, for the sake of knowledge workflow, one needs to find a compromise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to address this concern by devising a method that automatically enriches a folksonomy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the ontologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.}, author = {Marinho, Leandro Balby and Buza, Krisztian and Schmidt-Thieme, Lars}, booktitle = {International Semantic Web Conference}, crossref = {conf/semweb/2008}, date = {2008-10-24}, editor = {Sheth, Amit P. and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy W. and Thirunarayan, Krishnaprasad}, ee = {http://dx.doi.org/10.1007/978-3-540-88564-1_17}, file = {marinho2008folksonomybased.pdf:marinho2008folksonomybased.pdf:PDF}, groups = {public}, interhash = {d295e7d4615500c670e70ad240fada29}, intrahash = {cfa4c4520d4cf02e03dd3b84bb5c9578}, isbn = {978-3-540-88563-4}, pages = {261-276}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2010-03-30 16:14:58}, title = {Folksonomy-Based Collabulary Learning.}, url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2008.html#MarinhoBS08}, username = {dbenz}, volume = 5318, year = 2008 } @inproceedings{rattenbury2007towards, abstract = {We describe an approach for extracting semantics of tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website that supports time and location (latitude/longitude) metadata. We analyze two methods inspired by well-known burst-analysis techniques and one novel method: Scale-structure Identification. We evaluate the methods on a subset of Flickr data, and show that our Scale-structure Identification method outperforms the existing techniques. The approach and methods described in this work can be used in other domains such as geo-annotated web pages, where text terms can be extracted and associated with usage patterns.}, address = {New York, NY, USA}, author = {Rattenbury, Tye and Good, Nathaniel and Naaman, Mor}, booktitle = {SIGIR '07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {10.1145/1277741.1277762}, file = {rattenbury2007towards.pdf:rattenbury2007towards.pdf:PDF}, groups = {public}, interhash = {8b02d2b3fdbb97c3db6e3b23079a56e5}, intrahash = {bf6f73d2ef74ca6f1d355fb5688b673c}, isbn = {978-1-59593-597-7}, pages = {103--110}, publisher = {ACM Press}, timestamp = {2010-11-10 15:35:25}, title = {Towards automatic extraction of event and place semantics from flickr tags}, url = {http://dx.doi.org/10.1145/1277741.1277762}, username = {dbenz}, year = 2007 }