http://www.bibsonomy.org/burst/user/hotho/surveyBibSonomy BuRST Feed for /user/hotho/survey2008-07-26T21:17:59+02:00Handbook on ontologies [WorldCat.org]http://www.bibsonomy.org/bibtex/2ad25de339fffcc16870b813b6db00339/hothohotho2008-06-12T16:55:39+02:00semantic ontology web handbook survey <span style="color:#555555;">Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and Rudi <a href="http://www.bibsonomy.org/author/Studer">Studer</a> </span><em>Springer, </em><em>Berlin; New York, </em>(<em>2004</em>)Thu Jun 12 16:55:39 CEST 2008Berlin; New YorkInternational handbooks on information systems--Handbook on ontologies2004semantic ontology web handbook survey Institut AIFB - Publikation: Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Texthttp://www.bibsonomy.org/bibtex/2fe4c2950b5be221b493e29e4339240e8/hothohotho2008-05-20T17:43:51+02:00survey toread ontology learning <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> and Rudi <a href="http://www.bibsonomy.org/author/Studer">Studer</a> </span><em>Information, Wissenschaft und Praxis</em><em>57(6-7):315-320</em><em>OCT2006. </em><em>see the special issue for more contributions related to the Semantic Web . </em>Tue May 20 17:43:51 CEST 2008Information, Wissenschaft und PraxisOCTsee the special issue for more contributions related to the Semantic Web6-7315-320Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text572006survey toread ontology learning http://www.bibsonomy.org/bibtex/2ff99ff85fdc2224d826dab75df21cf0d/hothohotho2008-04-30T21:27:42+02:00image retrieval ir survey <span style="color:#555555;">Arnold W. M. <a href="http://www.bibsonomy.org/author/Smeulders">Smeulders</a> and Marcel <a href="http://www.bibsonomy.org/author/Worring">Worring</a> and Simone <a href="http://www.bibsonomy.org/author/Santini">Santini</a> and Amarnath <a href="http://www.bibsonomy.org/author/Gupta">Gupta</a> and Ramesh <a href="http://www.bibsonomy.org/author/Jain">Jain</a> </span><em>IEEE Trans. Pattern Anal. Mach. Intell.</em><em>22(12):1349--1380</em><em>December2000. </em>Wed Apr 30 21:27:42 CEST 2008Washington, DC, USAIEEE Trans. Pattern Anal. Mach. Intell.December121349--1380Content-Based Image Retrieval at the End of the Early Years222000image retrieval ir survey Content Based Image Retrieval CBIR Survey Paper - 2008http://www.bibsonomy.org/bibtex/2278a48194bc9afbd298c36dd497a9821/hothohotho2008-04-30T21:25:36+02:00images ir survey retrieval <span style="color:#555555;">Ritendra <a href="http://www.bibsonomy.org/author/Datta">Datta</a> and Dhiraj <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> and Jia <a href="http://www.bibsonomy.org/author/Li">Li</a> and James Z. <a href="http://www.bibsonomy.org/author/Wang">Wang</a> </span><em>ACM Computing Surveys</em>(<em>2008</em>)Wed Apr 30 21:25:36 CEST 2008ACM Computing Surveys2Image Retrieval: Ideas, Influences, and Trends of the New Age402008images ir survey retrieval We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this paper, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and discuss the spawning of related sub-fields in the process. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real-world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.Statistical Relational Learninghttp://www.bibsonomy.org/bibtex/21a4c5b5688b4295ad19bd72295c25ba2/hothohotho2007-08-31T09:29:11+02:00srl survey <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Domingos">Domingos</a> and Y. <a href="http://www.bibsonomy.org/author/Abe">Abe</a> and C. <a href="http://www.bibsonomy.org/author/Anderson">Anderson</a> and A. <a href="http://www.bibsonomy.org/author/Doan">Doan</a> and D. <a href="http://www.bibsonomy.org/author/Fox">Fox</a> and A. <a href="http://www.bibsonomy.org/author/Halevy">Halevy</a> and G. <a href="http://www.bibsonomy.org/author/Hulten">Hulten</a> and H. <a href="http://www.bibsonomy.org/author/Kautz">Kautz</a> and T. <a href="http://www.bibsonomy.org/author/Lau">Lau</a> and L. <a href="http://www.bibsonomy.org/author/Liao">Liao</a> and J. <a href="http://www.bibsonomy.org/author/Madhavan">Madhavan</a> and <a href="http://www.bibsonomy.org/author/Mausam">Mausam</a> and D. <a href="http://www.bibsonomy.org/author/Patterson">Patterson</a> and M. <a href="http://www.bibsonomy.org/author/Richardson">Richardson</a> and S. <a href="http://www.bibsonomy.org/author/Sanghai">Sanghai</a> and D. <a href="http://www.bibsonomy.org/author/Weld">Weld</a> and S. <a href="http://www.bibsonomy.org/author/Wolfman">Wolfman</a> </span><em>Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data, </em>(<em>2003</em>)Fri Aug 31 09:29:11 CEST 2007Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational DataResearch on Statistical Relational Learning at the University of Washington2003srl survey Jensen - SRL 2003http://www.bibsonomy.org/bibtex/26f4fe9dc01547709202ad6556423e863/hothohotho2007-08-31T09:01:47+02:00***** survey srl <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Neville">Neville</a> and M. <a href="http://www.bibsonomy.org/author/Rattigan">Rattigan</a> and D. <a href="http://www.bibsonomy.org/author/Jensen">Jensen</a> </span><em>Proceedings of the Workshop on Learning Statistical Models from Relational Data, Eighteenth International Joint Conference on Artificial Intelligence, </em>(<em>2003</em>)Fri Aug 31 09:01:47 CEST 2007Proceedings of the Workshop on Learning Statistical Models from Relational Data, Eighteenth International Joint Conference on Artificial IntelligenceStatistical relational learning: Four claims and a survey2003***** survey srl Statistical relational learning (SRL) research has made significant progress over the last 5 years. We have successfully demonstrated the feasibility of a number of probabilistic models for relational data, including probabilistic relational models, Bayesian logic programs, and relational probability trees, and the interest in SRL isgrowing. However, in order to sustain and nurture the growth of SRL as a subfield we need to refocus our efforts on the science of machine learning — moving from demonstrations to comparative and ablation studies. We will outline four assertions that are implicit to SRL research but which have been only minimally evaluated. We hope to stimulate discussion as to how, as a community, these claims can be addressed in future research.http://www.bibsonomy.org/bibtex/27ec15face46e6b45586701c7fed93eab/hothohotho2007-08-28T10:56:02+02:00survey sense disambiguation word nlp wsd <span style="color:#555555;">N. <a href="http://www.bibsonomy.org/author/Ide">Ide</a> and J. <a href="http://www.bibsonomy.org/author/Véronis">V&#233;ronis</a> </span><em>Computational Linguistics</em><em>24(1):1--40</em>(<em>1998</em>)Tue Aug 28 10:56:02 CEST 2007Computational Linguistics11--40{Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art}241998survey sense disambiguation word nlp wsd SpringerLink - Zeitschriftenbeitraghttp://www.bibsonomy.org/bibtex/2c26c16e0a8036000b788fada656f59dd/hothohotho2007-07-04T16:49:33+02:00activation search network *** spreading ir survey retrieval information semantic msn <span style="color:#555555;">F. <a href="http://www.bibsonomy.org/author/Crestani">Crestani</a> </span><em>Artificial Intelligence Review</em><em>11(6):453--482</em><em>December1997. </em>Wed Jul 04 16:49:33 CEST 2007Artificial Intelligence ReviewDecember6453--482Application of Spreading Activation Techniques in Information Retrieval111997activation search network *** spreading ir survey retrieval information semantic msn This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR. ER -imported from citeulikehttp://www.bibsonomy.org/bibtex/216373fb33b38c4769794ed7e7875f27e/hothohotho2007-06-05T11:31:52+02:00survey folksonomy tagging <span style="color:#555555;">Edith <a href="http://www.bibsonomy.org/author/Speller">Speller</a> </span><em>February2007. </em>Tue Jun 05 11:31:52 CEST 2007Library Student JournalFebruaryLibrary Student Journal: Collaborative tagging, folksonomies, distributed classification or ethnoclassification: a literature review.2007survey folksonomy tagging Tagging, folksonomy, distributed classification, ethnoclassification—however it is labelled, the concept of users creating and aggregating their own metadata is gaining ground on the internet. This literature review briefly defines the topic at hand, looking at current implementations and summarizing key advantages and disadvantages of distributed classification systems with reference to prominent folksonomy commentators. After considering whether distributed classification can replace expert catalogers entirely, it concludes that distributed classification can make an important contribution to digital information organisation, but that it may need to be integrated with more traditional organisation tools to overcome its current weaknesses.[cs/0701072] Tagging, Folksonomy & Co - Renaissance of Manual Indexing?http://www.bibsonomy.org/bibtex/29ec98351dc630ea6b1f65046ba44a8dd/hothohotho2007-03-30T16:35:57+02:00semantic bibsonomy tagging knowledge survey toread organisation km **** indexing folksonomy <span style="color:#555555;">Jakob <a href="http://www.bibsonomy.org/author/Voss">Voss</a> </span>(<em>2007</em>)Fri Mar 30 16:35:57 CEST 2007Tagging, Folksonomy \& Co - Renaissance of Manual Indexing?2007semantic bibsonomy tagging knowledge survey toread organisation km **** indexing folksonomy This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.http://www.bibsonomy.org/bibtex/292c03ba02a41f95ae315273939c8daa5/hothohotho2007-03-18T13:33:04+01:00clustering survey toread <span style="color:#555555;">Rui <a href="http://www.bibsonomy.org/author/Xu">Xu</a> and II <a href="http://www.bibsonomy.org/author/Wunsch">Wunsch</a> </span><em>Neural Networks, IEEE Transactions on</em><em>16(3):645--678</em>(<em>2005</em>)Sun Mar 18 13:33:04 CET 2007Neural Networks, IEEE Transactions on3645--678Survey of clustering algorithms162005clustering survey toread Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.A Survey of Web Information Extraction Systemshttp://www.bibsonomy.org/bibtex/2234a08916f1fd77df13ea731622333f7/hothohotho2007-03-01T11:43:38+01:00ie survey information extraction <span style="color:#555555;">Mohammed <a href="http://www.bibsonomy.org/author/Kayed">Kayed</a> and Khaled F. <a href="http://www.bibsonomy.org/author/Shaalan">Shaalan</a> </span><em>IEEE Transactions on Knowledge and Data Engineering</em><em>18(10):1411--1428</em>(<em>2006</em>) <em>Member-Chia-Hui Chang and Member-Moheb Ramzy Girgis . </em>Thu Mar 01 11:43:38 CET 2007Piscataway, NJ, USAIEEE Transactions on Knowledge and Data EngineeringMember-Chia-Hui Chang and Member-Moheb Ramzy Girgis101411--1428A Survey of Web Information Extraction Systems182006ie survey information extraction dblphttp://www.bibsonomy.org/bibtex/21016d42a26ca0748ba227d62b7126933/hothohotho2007-02-28T15:36:18+01:00web mining survey <span style="color:#555555;">Sanjay Kumar <a href="http://www.bibsonomy.org/author/Madria">Madria</a> and Sourav S. <a href="http://www.bibsonomy.org/author/Bhowmick">Bhowmick</a> and Wee Keong <a href="http://www.bibsonomy.org/author/Ng">Ng</a> and Ee-Peng <a href="http://www.bibsonomy.org/author/Lim">Lim</a> </span><em>DaWaK, </em><em>volume1676ofLecture Notes in Computer Science, </em><em>page303-312. </em><em>Springer, </em>(<em>1999</em>)Wed Feb 28 15:36:18 CET 2007DaWaKconf/dawak/1999303-312Lecture Notes in Computer ScienceResearch Issues in Web Data Mining.16761999web mining survey http://www.bibsonomy.org/bibtex/2b6fd1311beb153161ab89f7d64f5ff70/hothohotho2007-02-26T15:33:38+01:00cbr survey <span style="color:#555555;">Brigitte <a href="http://www.bibsonomy.org/author/Bartsch-Spörl">Bartsch-Sp&#246;rl</a> and Mario <a href="http://www.bibsonomy.org/author/Lenz">Lenz</a> and Andr&#233; <a href="http://www.bibsonomy.org/author/Hübner">H&#252;bner</a> </span><em>XPS-99: Knowledge-Based Systems - Survey and Future Directions,, </em><em>volume1570ofLecture Notes in Computer Science, </em><em>page67-89. </em><em>Springer, </em>(<em>1999</em>)Mon Feb 26 15:33:38 CET 2007XPS-99: Knowledge-Based Systems - Survey and Future Directions,67-89Lecture Notes in Computer ScienceCase-Based Reasoning: Survey and Future Directions.15701999cbr survey http://www.bibsonomy.org/bibtex/20fe0d5dd12c2cb59dfc330e684ec4b4a/hothohotho2006-10-27T11:56:43+02:00text tm categorization survey ml classification <span style="color:#555555;">F. <a href="http://www.bibsonomy.org/author/Sebastiani">Sebastiani</a> </span><em>ACM Computing Surveys</em><em>34(1):1--47</em>(<em>2002</em>)Fri Oct 27 11:56:43 CEST 2006ACM Computing Surveys11--47Machine learning in automated text categorization342002text tm categorization survey ml classification http://www.bibsonomy.org/bibtex/26ecc8a3cee1a99bbb9f8f8dd6a9d2959/hothohotho2006-06-27T09:41:56+02:00overview SumSchool06 myown mining survey text ontology 2005 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Andreas <a href="http://www.bibsonomy.org/author/Nürnberger">N&#252;rnberger</a> and Gerhard <a href="http://www.bibsonomy.org/author/Paaß">Paa&#223;</a> </span><em>LDV Forum - GLDV Journal for Computational Linguistics and Language Technology</em><em>20(1):19-62</em><em>MAY2005. </em>Tue Jun 27 09:41:56 CEST 2006LDV Forum - GLDV Journal for Computational Linguistics and Language TechnologyMAY119-62 A Brief Survey of Text Mining202005overview SumSchool06 myown mining survey text ontology 2005 sdasdahttp://www.bibsonomy.org/bibtex/2d28c962a3a1f5219a3c25b5f7371e13d/hothohotho2006-05-17T18:22:08+02:00survey network learning tutorial bayes <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Heckerman">Heckerman</a> </span><em>Microsoft Research, </em><em>Redmond, Washington, </em>(<em>1995</em>)Wed May 17 18:22:08 CEST 2006Redmond, WashingtonA tutorial on learning with bayesian networks1995survey network learning tutorial bayes Survey Of Clustering Data Mining Techniques - Berkhin (ResearchIndex)http://www.bibsonomy.org/bibtex/22fec60df240f69dbf677e34825d20491/hothohotho2006-05-09T09:51:38+02:002006 lecture survey clustering kdd <span style="color:#555555;">Pavel <a href="http://www.bibsonomy.org/author/Berkhin">Berkhin</a> </span><em>Accrue Software, </em><em>San Jose, CA, </em>(<em>2002</em>)Tue May 09 09:51:38 CEST 2006San Jose, CASurvey Of Clustering Data Mining Techniques20022006 lecture survey clustering kdd http://www.bibsonomy.org/bibtex/2cdeed04e46c23f5c22add8a192d9c48b/hothohotho2006-04-18T14:59:58+02:00relational learning ml reinforcement survey <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/van Otterlo">van Otterlo</a> </span><em>July2006. </em>Tue Apr 18 14:59:58 CEST 2006JulyA Survey of Reinforcement Learning in Relational Domains2006relational learning ml reinforcement survey http://www.bibsonomy.org/bibtex/23e79734ee1a6e49aee02ffd108224d1c/hothohotho2006-04-17T19:22:02+02:00kdd lecture ml survey introduction 2006 dm <span style="color:#555555;">Tom M. <a href="http://www.bibsonomy.org/author/Mitchell">Mitchell</a> </span><em>McGraw-Hill, </em>(<em>1997</em>)Mon Apr 17 19:22:02 CEST 2006Machine Learning1997kdd lecture ml survey introduction 2006 dm