@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 } @inproceedings{wu2006exploring, abstract = {In order to obtain a machine understandable semantics for web resources, research on the Semantic Web tries to an- notate web resources with concepts and relations from ex- plicitly de¯ned formal ontologies. This kind of formal an- notation is usually done manually or semi-automatically. In this paper, we explore a complement approach that focuses on the \social annotations of the web" which are annota- tions manually made by normal web users without a pre- de¯ned formal ontology. Compared to the formal annota- tions, although social annotations are coarse-grained, infor- mal and vague, they are also more accessible to more peo- ple and better re°ect the web resources' meaning from the users' point of views during their actual usage of the web re- sources. Using a social bookmark service as an example, we show how emergent semantics [2] can be statistically derived from the social annotations. Furthermore, we apply the de- rived emergent semantics to discover and search shared web bookmarks. The initial evaluation on our implementation shows that our method can e®ectively discover semantically related web bookmarks that current social bookmark service can not discover easily.}, address = {New York, NY, USA}, author = {Wu, Xian and Zhang, Lei and Yu, Yong}, booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web}, file = {wu2006exploring.pdf:wu2006exploring.pdf:PDF}, groups = {public}, interhash = {478741551c92402f539a90a9caed61b6}, intrahash = {2ff38a7f8e9e3941d0598877fe964eb5}, lastdatemodified = {2007-01-04}, lastname = {Wu}, own = {notown}, pages = {417--426}, pdf = {wu06-exploring.pdf}, publisher = {ACM Press}, read = {notread}, timestamp = {2007-09-11 13:31:41}, title = {Exploring social annotations for the semantic web}, url = {http://doi.acm.org/10.1145/1135777.1135839}, username = {dbenz}, year = 2006 } @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 } @article{zhang2006emergent, abstract = {Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology provides a shared and machine understandable semantics for web resources that agents and applications can utilize. This top-down approach (in the sense that an ontology is defined first on top of existing web resources and then used later to markup them), however, has a high barrier to entry and is difficult to scale up. In this paper, we investigate using a bottom-up approach for semantically annotating web resources as supported by the now widely popular social bookmarks services on the web where users can annotate and categorize web resources using �tags� freely choosen by the user without any pre-existing global semantic model. This kind of informal social categories is coined as �folksonomies�. We show how global semantics can be statistically inferred from the folksonomies to semantically annotate the web resources. The global semantic model also disambiguate the tags and group synonymous tags together. Finally, we show that there indeed are hierarchical relations among the emerged concepts in the folksonomy and it is plausible to further identify them if we use more advanced probabilistic models.}, author = {Zhang, Lei and Wu, Xian and Yu, Yong}, dateadded = {2007-01-04}, file = {zhang2006emergent.pdf:zhang2006emergent.pdf:PDF}, groups = {public}, interhash = {bf08902c01dd395ec83cc9b7264a6099}, intrahash = {55fd3b7ef57219ef04a9e5c904c94321}, journal = {Journal on Data Semantics VI}, journalpub = {1}, lastdatemodified = {2007-01-04}, lastname = {Zhang}, own = {notown}, pdf = {zhang06-emergent.pdf}, read = {notread}, timestamp = {2007-09-11 13:31:42}, title = {Emergent Semantics from Folksonomies: A Quantitative Study}, url = {http://www.springerlink.com/content/vk81621n01506652/?p=7403d6673c664a2a97131596e47ddc88&pi=7}, username = {dbenz}, year = 2006 } @inproceedings{1031192, abstract = {The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.}, address = {New York, NY, USA}, author = {Xue, Gui-Rong and Zeng, Hua-Jun and Chen, Zheng and Yu, Yong and Ma, Wei-Ying and Xi, WenSi and Fan, WeiGuo}, booktitle = {CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management}, doi = {10.1145/1031171.1031192}, interhash = {31bb65c1b57888b0529c1a11e981bbe8}, intrahash = {2c9841b484ade7e9a8c9220662190c16}, isbn = {1-58113-874-1}, location = {Washington, D.C., USA}, pages = {118--126}, publisher = {ACM}, title = {Optimizing web search using web click-through data}, url = {http://portal.acm.org/citation.cfm?id=1031171.1031192}, year = 2004 } @inproceedings{Zhou/2007/Unsupervised, abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a 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 unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency.}, address = {Berlin, Heidelberg}, author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong}, booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea}, crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings}, editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe}, interhash = {af21595ee9f4a13b5e651ad049f31262}, intrahash = {355fcbb32255f3ba5f41819c00c520ba}, month = {November}, pages = {673--686}, publisher = {Springer Verlag}, series = {LNCS}, title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations}, url = {http://iswc2007.semanticweb.org/papers/673.pdf}, volume = 4825, year = 2007 } @inproceedings{Bao_2007, abstract = {This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding web pages; 2) the count of annotations indicates the popularity of web pages. Two novel algorithms are proposed to incorporate the above information into page ranking: 1) SocialSimRank (SSR) calculates the similarity between social annotations and web queries; 2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web users’ perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawled from del.icio.us. Experiments show that both SSR and SPR benefit web search significantly.}, address = {New York, NY, USA}, author = {Bao, Shenghua and Xue, Guirong and Wu, Xiaoyuan and Yu, Yong and Fei, Ben and Su, Zhong}, booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web}, interhash = {2cbdc7da88c90ef22468108c1f481159}, intrahash = {b9966b9df0199a0b7b2d5a1b0d7560cb}, privnote = {Models for search using social annotations: similarity ranking (tags as summaries of documents) and static ranking (number of annotations). Use delicious and ODP data.}, publisher = {ACM Press}, title = {Optimizing web search using social annotations}, year = 2007 } @article{zhang2006emergent, author = {Zhang, Lei and Wu, Xian and Yu, Yong}, interhash = {bf08902c01dd395ec83cc9b7264a6099}, intrahash = {628d58d19806ef46ca68cb45ca057e74}, journal = {Special issue of Journal of Data Semantics on Emergent Semantics (to appear)}, title = {Emergent Semantics from Folksonomies: A Quantitative Study}, year = 2006 } @inproceedings{1135839, address = {New York, NY, USA}, author = {Wu, Xian and Zhang, Lei and Yu, Yong}, booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web}, doi = {http://doi.acm.org/10.1145/1135777.1135839}, interhash = {478741551c92402f539a90a9caed61b6}, intrahash = {2ff38a7f8e9e3941d0598877fe964eb5}, isbn = {1-59593-323-9}, location = {Edinburgh, Scotland}, pages = {417--426}, publisher = {ACM Press}, title = {Exploring social annotations for the semantic web}, year = 2006 }