@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{Kim2008, address = {Berlin, Deutschland}, author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee}, booktitle = {{Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}}, interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc}, intrahash = {7d3c3c2189394a8686ca9812d58bfe74}, pages = {128--137}, publisher = {{Dublin Core Metadata Initiative}}, title = {{The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}}, year = 2008 } @inproceedings{afsharchi2006automated, abstract = {This research addresses the formation of new concepts and their corresponding ontology in a multi-agent system where individual autonomous agents try to learn new concepts by consulting several other agents. In this research individual agents create and learn their distinct conceptualization and rather than a commitment to a common ontology they use their own ontologies. In this paper multi-agent supervised learning of concepts among individual agents with diverse conceptualization and different ontologies is introduced and demonstrated through an intuitive example in which supervisors are other agents rather than a human.}, acmid = {1146863}, address = {New York, NY, USA}, articleno = {16}, author = {Afsharchi, Mohsen and Far, Behrouz H.}, booktitle = {Proceedings of the 1st international conference on Scalable information systems}, doi = {10.1145/1146847.1146863}, interhash = {3614f61a4bddc48c0eeb7eecf6e7adee}, intrahash = {b5528a701397b534b3b0e5a24e37e7e2}, isbn = {1-59593-428-6}, location = {Hong Kong}, publisher = {ACM}, series = {InfoScale '06}, title = {Automated ontology evolution in a multi-agent system}, url = {http://doi.acm.org/10.1145/1146847.1146863}, year = 2006 } @inproceedings{le2007current, abstract = {Ontologies are widely used and play important roles in applications related to knowledge management, artificial intelligence, natural language processing, etc. Measuring the semantic similarity between ontological concepts is necessary in applications that use ontologies. This paper presents a survey of approaches to compute ontological concept similarity. A taxonomy showing the classification of approaches is introduced. The advantages and disadvantages of each approach are discussed.}, author = {Le, Duy Ngan and Goh, A.E.S.}, doi = {10.1109/SKG.2007.16}, interhash = {abe9003dbe2bc43bef22e4249f55746a}, intrahash = {356c507e72532460c1886974fa04d4c4}, journal = {Semantics, Knowledge and Grid, Third International Conference on}, month = {Oct.}, pages = {266-269}, title = {Current Practices in Measuring Ontological Concept Similarity}, year = 2007 } @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 } @article{zhou2007ontology, 0 = {http://dx.doi.org/10.1007/s10799-007-0019-5}, abstract = {Abstract\ \ Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.}, at = {2009-02-13 15:22:56}, author = {Zhou, Lina}, doi = {10.1007/s10799-007-0019-5}, file = {zhou2007ontology.pdf:zhou2007ontology.pdf:PDF}, groups = {public}, interhash = {78b6d3db998dcd27c475dfff3816f48f}, intrahash = {95b0f4f7c9c628e032d8bb4c69b432ed}, journal = {Information Technology and Management}, journalpub = {1}, misc_id = {1719627}, number = 3, pages = {241--252}, priority = {3}, timestamp = {2010-06-01 16:18:37}, title = {Ontology learning: state of the art and open issues}, url = {http://www.springerlink.com/content/j4g22112l7k00833/}, username = {dbenz}, volume = 8, year = 2007 } @proceedings{30474, author = {Tresp, Volker and Bundschus, Markus and Rettinger, Achim and Huang, Yi}, interhash = {e27fbf5b5fb16f66cd0c7a3932fc4695}, intrahash = {006468688804bc3563225b8dcd7aea97}, journal = {Uncertainty Reasoning for the Semantic Web I Lecture Notes in AI}, publisher = {Springer}, title = {Towards machine learning on the semantic web}, url = {http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/LearningRDF23.pdf}, year = 2008 } @article{4686305, abstract = {Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.}, author = {Limpens, Freddy and Gandon, Fabien and Buffa, Michel}, doi = {10.1109/ASEW.2008.4686305}, interhash = {cb1d534be80d664a50df66e8977b774e}, intrahash = {9372f9c2db8b9f4cf05b3db84e6589ac}, journal = {Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on}, month = {Sept.}, pages = {13-18}, title = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey}, year = 2008 } @article{1282, author = {Cimiano, Philipp and Völker, Johanna and Studer, Rudi}, interhash = {aeb553dc2e190f0a5974dfdc709d450a}, intrahash = {fe4c2950b5be221b493e29e4339240e8}, journal = {Information, Wissenschaft und Praxis}, month = OCT, note = {see the special issue for more contributions related to the Semantic Web}, number = {6-7}, pages = {315-320}, title = {Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text}, url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/pci/Publications/iwp06.pdf}}, volume = 57, year = 2006 } @article{McRae:2005:Behav-Res-Methods:16629288, abstract = {Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.}, author = {McRae, K and Cree, G S and Seidenberg, M S and McNorgan, C}, interhash = {209f2874b49fd74fce1fe0ad645733e2}, intrahash = {936af12b025e37b0a6aac6bc103f58a3}, journal = {Behav Res Methods}, month = Nov, number = 4, pages = {547-559}, pmid = {16629288}, title = {Semantic feature production norms for a large set of living and nonliving things}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16629288}, volume = 37, year = 2005 } @article{isafolksonomy2007fwn, author = {Noruzi, Alireza}, interhash = {406153e7b2a8c11a963d7f14718f02d7}, intrahash = {eaef17fef76ad3152f0300a5e9d5ddae}, journal = {Webology}, number = 2, title = {{Folksonomies: Why do we need controlled vocabulary?}}, url = {http://www.webology.ir/2007/v4n2/editorial12.html}, volume = 4, year = 2007 } @inproceedings{schmitz06, author = {Schmitz, Patrick}, booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland}, interhash = {1335f4ef87f951e6edf4fd94f885d3a2}, intrahash = {77143fd854a06583430afae1371fad71}, month = May, title = {Inducing Ontology from Flickr Tags.}, url = {http://www.ibiblio.org/www_tagging/2006/22.pdf}, year = 2006 } @inproceedings{dellschaft2006GoldEvalOntoLearn, address = {Athens, GA, USA}, author = {Dellschaft, Klaas and Staab, Steffen}, booktitle = {In: Proc. of ISWC-2006 International Semantic Web Conference}, interhash = {bd5dcdc47711f5dce1a2546db5b66e79}, intrahash = {0bfd502e363ef3f1523d77f972f08397}, month = {November}, pdf = {http://www.uni-koblenz.de/~staab/Research/Publications/2006/DellschaftStaabISWCsubmitted.pdf}, publisher = {Springer, LNCS}, title = {On How to Perform a Gold Standard based Evaluation of Ontology Learning}, url = {http://iswc2006.semanticweb.org/items/paper_44.php}, year = 2006 }