@inproceedings{mitchell2015, author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.}, booktitle = {AAAI}, interhash = {52d0d71f6f5b332dabc1412f18e3a93d}, intrahash = {63070703e6bb812852cca56574aed093}, note = {: Never-Ending Learning in AAAI-2015}, title = {Never-Ending Learning}, url = {http://www.cs.cmu.edu/~wcohen/pubs.html}, year = 2015 } @inproceedings{yuen2009survey, abstract = {Human computation is a technique that makes use of human abilities for computation to solve problems. The human computation problems are the problems those computers are not good at solving but are trivial for humans. In this paper, we give a survey of various human computation systems which are categorized into initiatory human computation, distributed human computation and social game-based human computation with volunteers, paid engineers and online players. For the existing large number of social games, some previous works defined various types of social games, but the recent developed social games cannot be categorized based on the previous works. In this paper, we define the categories and the characteristics of social games which are suitable for all existing ones. Besides, we present a survey on the performance aspects of human computation system. This paper gives a better understanding on human computation system.}, author = {Yuen, Man-Ching and Chen, Ling-Jyh and King, I.}, booktitle = {Proceedings of the International Conference on Computational Science and Engineering, CSE '09}, doi = {10.1109/CSE.2009.395}, interhash = {69f9bd3e6a721f226e39e1f990e20286}, intrahash = {8670a20dbf6aa9dd21da81ab78a1e333}, month = aug, pages = {723--728}, title = {A Survey of Human Computation Systems}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5283450&tag=1}, volume = 4, year = 2009 } @inproceedings{conf/sigir/GuanBMCW09, author = {Guan, Ziyu and Bu, Jiajun and Mei, Qiaozhu and Chen, Chun and Wang, Can}, booktitle = {SIGIR}, crossref = {conf/sigir/2009}, editor = {Allan, James and Aslam, Javed A. and Sanderson, Mark and Zhai, ChengXiang and Zobel, Justin}, ee = {http://doi.acm.org/10.1145/1571941.1572034}, interhash = {53d2e8bc966048bc01efcc57b2fc8250}, intrahash = {ac9427acf51cbf7cb5a35f66a16a32c0}, isbn = {978-1-60558-483-6}, pages = {540-547}, publisher = {ACM}, title = {Personalized tag recommendation using graph-based ranking on multi-type interrelated objects.}, url = {http://www-personal.umich.edu/~qmei/pub/sigir09-tag.pdf}, year = 2009 } @inproceedings{hu2008enhancing, author = {Hu, Jian and Fang, Lujun and Cao, Yang and Zeng, Hua-Jun and Li, Hua and Yang, Qiang and Chen, Zheng}, booktitle = {SIGIR}, crossref = {conf/sigir/2008}, editor = {Myaeng, Sung-Hyon and Oard, Douglas W. and Sebastiani, Fabrizio and Chua, Tat-Seng and Leong, Mun-Kew}, ee = {http://doi.acm.org/10.1145/1390334.1390367}, interhash = {0a2878165034dcdfacb9045608ec482a}, intrahash = {76f863a12c0b983ec67682deaec1ada4}, isbn = {978-1-60558-164-4}, pages = {179-186}, publisher = {ACM}, title = {Enhancing text clustering by leveraging Wikipedia semantics.}, url = {http://dblp.uni-trier.de/db/conf/sigir/sigir2008.html#HuFCZLYC08}, year = 2008 } @inproceedings{tang2009towards, abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.}, address = {San Francisco, CA, USA}, author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin}, booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence}, file = {tang2009towards.pdf:tang2009towards.pdf:PDF}, groups = {public}, interhash = {17f95a6ba585888cf45443926d8b7e98}, intrahash = {7b335f08a288a79eb70eff89f1ec7630}, location = {Pasadena, California, USA}, pages = {2089--2094}, publisher = {Morgan Kaufmann Publishers Inc.}, timestamp = {2009-12-23 21:30:44}, title = {Towards ontology learning from folksonomies}, url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf}, username = {dbenz}, year = 2009 } @inproceedings{conf/sigir/HuFCZLYC08, author = {Hu, Jian and Fang, Lujun and Cao, Yang and Zeng, Hua-Jun and Li, Hua and Yang, Qiang and Chen, Zheng}, booktitle = {SIGIR}, crossref = {conf/sigir/2008}, date = {2008-07-27}, editor = {Myaeng, Sung-Hyon and Oard, Douglas W. and Sebastiani, Fabrizio and Chua, Tat-Seng and Leong, Mun-Kew}, ee = {http://doi.acm.org/10.1145/1390334.1390367}, interhash = {0a2878165034dcdfacb9045608ec482a}, intrahash = {76f863a12c0b983ec67682deaec1ada4}, isbn = {978-1-60558-164-4}, pages = {179-186}, publisher = {ACM}, title = {Enhancing text clustering by leveraging Wikipedia semantics.}, url = {http://dblp.uni-trier.de/db/conf/sigir/sigir2008.html#HuFCZLYC08}, year = 2008 } @inproceedings{1661779, abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.}, address = {San Francisco, CA, USA}, author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin}, booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence}, interhash = {17f95a6ba585888cf45443926d8b7e98}, intrahash = {7b335f08a288a79eb70eff89f1ec7630}, location = {Pasadena, California, USA}, pages = {2089--2094}, publisher = {Morgan Kaufmann Publishers Inc.}, title = {Towards ontology learning from folksonomies}, url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf}, year = 2009 } @inproceedings{1458233, address = {New York, NY, USA}, author = {Chen, Keke and Lu, Rongqing and Wong, C. K. and Sun, Gordon and Heck, Larry and Tseng, Belle}, booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management}, doi = {http://doi.acm.org/10.1145/1458082.1458233}, interhash = {ab045aa8016700e8a7c93f5c55dc91fe}, intrahash = {0e3e57ca0edda99c53dc3101ffeaef96}, isbn = {978-1-59593-991-3}, location = {Napa Valley, California, USA}, pages = {1143--1152}, publisher = {ACM}, title = {Trada: tree based ranking function adaptation}, url = {http://portal.acm.org/citation.cfm?doid=1458082.1458233}, year = 2008 } @article{chen:058701, author = {Chen, Yiping and Paul, Gerald and Havlin, Shlomo and Liljeros, Fredrik and Stanley, H. Eugene}, doi = {10.1103/PhysRevLett.101.058701}, eid = {058701}, interhash = {591effe237db9e7b8443c05390e5a6f4}, intrahash = {3409d4e03990b0ff2a9704b665adf16e}, journal = {Physical Review Letters}, number = 5, numpages = {4}, pages = 058701, publisher = {APS}, title = {Finding a Better Immunization Strategy}, url = {http://link.aps.org/abstract/PRL/v101/e058701}, volume = 101, year = 2008 }