@incollection{christiaens2006metadata, abstract = {In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.}, author = {Christiaens, Stijn}, booktitle = {Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops}, file = {christiaens2006metadata.pdf:christiaens2006metadata.pdf:PDF}, interhash = {f733d993459329ed1ef9f26d303ba0d9}, intrahash = {efc1396e845f3db1688dc8ef154d9520}, lastdatemodified = {2007-01-04}, lastname = {Christiaens}, own = {notown}, pdf = {christiaens06-metadata.pdf}, publisher = {Springer}, read = {notread}, title = {Metadata Mechanisms: From Ontology to Folksonomy ... and Back}, url = {http://www.springerlink.com/content/m370107220473394}, year = 2006 } @article{limpens2008bridging, 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}, file = {limpens2008bridging.pdf:limpens2008bridging.pdf:PDF}, groups = {public}, interhash = {cb1d534be80d664a50df66e8977b774e}, intrahash = {9372f9c2db8b9f4cf05b3db84e6589ac}, journal = {Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on}, journalpub = {1}, month = {Sept.}, pages = {13-18}, timestamp = {2009-07-24 14:21:18}, title = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey}, 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 } @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{cimiano2006ontologies, abstract = {Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies,which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies ‘on demand’.}, author = {Cimiano, Philipp and Völker, Johanna and Studer, Rudi}, file = {cimiano2006ontologies.pdf:cimiano2006ontologies.pdf:PDF}, groups = {public}, interhash = {aeb553dc2e190f0a5974dfdc709d450a}, intrahash = {fe4c2950b5be221b493e29e4339240e8}, journal = {Information, Wissenschaft und Praxis}, journalpub = {1}, month = OCT, note = {see the special issue for more contributions related to the Semantic Web}, number = {6-7}, pages = {315-320}, timestamp = {2008-07-23 11:47:29}, 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}}, username = {dbenz}, volume = 57, year = 2006 } @inproceedings{haase2005collaborative, abstract = {Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.}, author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York}, booktitle = {ESWC}, crossref = {conf/esws/2005}, date = {2005-05-24}, editor = {Gómez-Pérez, Asunción and Euzenat, Jérôme}, ee = {http://dx.doi.org/10.1007/11431053_33}, file = {haase2005collaborative.pdf:haase2005collaborative.pdf:PDF}, groups = {public}, interhash = {c9ba81293a1b27f1c9bdf38a3beec060}, intrahash = {1a8829cde1cb26241a48901e28a953d2}, isbn = {3-540-26124-9}, pages = {486-499}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2009-11-10 11:30:42}, title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.}, url = {http://www.aifb.uni-karlsruhe.de/WBS/pha/publications/collaborative05eswc.pdf}, username = {dbenz}, volume = 3532, year = 2005 } @inproceedings{halpin2006dynamics, abstract = {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classi- fication schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from �popular� tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.}, author = {Halpin, Harry and Robu, Valentin and Shepard, Hana}, booktitle = {Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)}, file = {halpin2006dynamics.pdf:halpin2006dynamics.pdf:PDF}, groups = {public}, interhash = {86b08d03b5f0bd947fd9095dc2c9a70c}, intrahash = {266b31ad3599499aacf593e82e775c5b}, lastdatemodified = {2007-01-04}, lastname = {Halpin}, own = {notown}, pdf = {halpin06-dynamics.pdf}, read = {notread}, timestamp = {2007-05-25 16:05:53}, title = {The Dynamics and Semantics of Collaborative Tagging}, username = {dbenz}, year = 2006 } @inproceedings{laniado2007using, abstract = {As the volume of information in the read-write Web increases rapidly, folksonomies are becoming a widely used tool to organize and categorize resources in a bottom up, flat and inclusive way. However, due to their very structure, they show some drawbacks; in particular the lack of hierarchy bears some limitations in the possibilities of searching and browsing. In this paper we investigate a new approach, based on the idea of integrating an ontology in the navigation interface of a folksonomy, and we describe an application that filters del.icio.us keywords through the WordNet hierarchy of concepts, to enrich the possibilities of navigation.}, author = {Laniado, David and Eynard, Davide and Colombetti, Marco}, booktitle = {Semantic Web Application and Perspectives - Fourth Italian Semantic Web Workshop}, file = {laniado2007using.pdf:laniado2007using.pdf:PDF}, groups = {public}, interhash = {20cfb04df242c1ab1c986128c9f5a9c9}, intrahash = {7f7ac73677841b4580461d408e83495a}, month = Dec, pages = {192--201}, timestamp = {2009-07-24 14:20:02}, title = {Using WordNet to turn a folksonomy into a hierarchy of concepts}, url = {http://home.dei.polimi.it/eynard/papers/swap2007.pdf}, username = {dbenz}, year = 2007 } @inproceedings{ponzetto2007deriving, abstract = {We take the category system inWikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.}, author = {Ponzetto, Simone Paolo and Strube, Michael}, booktitle = {AAAI}, crossref = {conf/aaai/2007}, date = {2007-09-05}, file = {ponzetto2007deriving.pdf:ponzetto2007deriving.pdf:PDF}, groups = {public}, interhash = {bc3a144ed8d3f2941359ae97a5b93194}, intrahash = {5db72406c5681facd7ad47895937d86e}, isbn = {978-1-57735-323-2}, pages = {1440-1445}, publisher = {AAAI Press}, timestamp = {2010-03-30 16:07:36}, title = {Deriving a Large-Scale Taxonomy from Wikipedia.}, url = {http://dblp.uni-trier.de/db/conf/aaai/aaai2007.html#PonzettoS07}, username = {dbenz}, year = 2007 } @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 } @inproceedings{silva2009semiautomatic, abstract = {This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing (NLP) and other machine learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.}, author = {Silva, L. De and Jayaratne, L.}, booktitle = {Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the}, doi = {10.1109/ICADIWT.2009.5273871}, file = {silva2009semiautomatic.pdf:silva2009semiautomatic.pdf:PDF}, groups = {public}, interhash = {c1996cb9e69de56e2bb2f8e763fe0482}, intrahash = {66bec053541e521fbe68c0119806ae49}, month = {Aug.}, pages = {446-451}, timestamp = {2010-02-23 12:54:40}, title = {Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116}, username = {dbenz}, year = 2009 } @inproceedings{tane2003courseware, abstract = {Topics in education are changing with an ever faster pace. E-Learningresources tend to be more and more decentralised. Users need increasingly to be able touse the resources of the web. For this, they should have tools for finding and organizinginformation in a decentral way. In this, paper, we show how an ontology-based toolsuite allows to make the most of the resources available on the web.}, author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd and Staab, Steffen and Studer, R.}, booktitle = {Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität}, editor = {David, Klaus and Wegner, Lutz}, file = {tane2003courseware.pdf:tane2003courseware.pdf:PDF}, groups = {public}, interhash = {7f33080bb78d089b24bf51c059f8f018}, intrahash = {850949481723b7dd03768ccd96b25cb9}, month = {November}, pages = {93-104}, publisher = {Kassel University Press}, timestamp = {2010-11-10 15:35:25}, title = {The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf}, username = {dbenz}, year = 2003 } @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 } @inproceedings{sorg2008cirwesa, author = {Sorg, Philipp and Cimiano, Philipp}, booktitle = {Working Notes for the CLEF 2008 Workshop}, evastar_pdf = {2008_1837_Sorg_Cross-lingual_I_1.pdf}, interhash = {7063c5bdff3528939877aa07ae4a35de}, intrahash = {27c1c374a750725824118ac02ba5f2c6}, title = {Cross-lingual Information Retrieval with Explicit Semantic Analysis}, type = {Inproceedings}, url = {http://www.aifb.kit.edu/images/7/7c/2008_1837_Sorg_Cross-lingual_I_1.pdf}, year = 2008 } @inproceedings{conf/ecai/HjelmB08, author = {Hjelm, Hans and Buitelaar, Paul}, booktitle = {ECAI}, crossref = {conf/ecai/2008}, editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.}, ee = {http://dx.doi.org/10.3233/978-1-58603-891-5-288}, interhash = {21a658154fb1a02e773b7a678b15f9f4}, intrahash = {813903a333a40ecf9a59ded552acb323}, isbn = {978-1-58603-891-5}, pages = {288-292}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.}, url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf}, volume = 178, year = 2008 } @incollection{cimiano2009ontology, abstract = {Ontology learning techniques serve the purpose of supporting an ontology engineer in the task of creating and maintaining an ontology. In this chapter, we present a comprehensive and concise introduction to the field of ontology learning. We present a generic architecture for ontology learning systems and discuss its main components. In addition, we introduce the main problems and challenges addressed in the field and give an overview of the most important methods applied. We conclude with a brief discussion of advanced issues which pose interesting challenges to the state-of-the-art.}, author = {Cimiano, P. and Mädche, A. and Staab, S. and Völker, J.}, booktitle = {Handbook on Ontologies}, edition = {2nd revised edition}, editor = {Staab, S. and Studer, R.}, interhash = {5387f28040285a086ab706bc33e7d7af}, intrahash = {f9f8bb0af1a8a514c270f83237313ac7}, pages = {245--267}, publisher = {Springer}, series = {International Handbooks on Information Systems}, title = {Ontology Learning}, url = {http://www.uni-koblenz.de/~staab/Research/Publications/2009/handbookEdition2/ontology-learning-handbook2.pdf}, year = 2009 } @article{eda2009effectiveness, abstract = {In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like “cool” and “fun” from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.}, author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu}, doi = {10.1007/s11280-009-0069-1}, interhash = {a560796c977bc7582017f662bf88c16d}, intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96}, issn = {1386-145X}, journal = {World Wide Web}, number = 4, pages = {421--440}, publisher = {Springer Netherlands}, title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.}, url = {http://dx.doi.org/10.1007/s11280-009-0069-1}, volume = 12, year = 2009 } @inproceedings{kim2008state, abstract = {There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.}, author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee}, booktitle = {DCMI '08: Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}, interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc}, intrahash = {cb56167e7e5e0dbfee017671064ff81e}, location = {Berlin, Germany}, pages = {128--137}, publisher = {Dublin Core Metadata Initiative}, title = {The state of the art in tag ontologies: a semantic model for tagging and folksonomies}, url = {http://portal.acm.org/citation.cfm?id=1503418.1503431}, year = 2008 } @article{lux2008folksonomies, 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}, interhash = {5dde7a91231320f96c0c4b3e7ba9a503}, intrahash = {dd5cdcc6449d97622033bbebcd4d1874}, journal = {International Journal of Knowledge and Learning}, month = jan, number = {4-5}, pages = {515--528}, 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}, volume = 3, year = 2008 } @inproceedings{benz2010semantics, address = {Raleigh, NC, USA}, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, interhash = {dbd2ac30cfb0faa29413275afc9b4387}, intrahash = {ba43b0db4b8f7cb091fd55d59e170477}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, year = 2010 }