@inproceedings{benz2010semantics, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, address = {Raleigh, NC, USA}, year = {2010}, keywords = {2010, myown, ol, ontology, semantics, websci, websci10} }

%0 = inproceedings %A = Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 2nd Web Science Conference (WebSci10) %C = Raleigh, NC, USA %D = 2010 %T = Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge

Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.

@article{1672980, author = {Jiang, Xing and Tan, Ah-Hwee}, title = {CRCTOL: A semantic-based domain ontology learning system}, journal = {J. Am. Soc. Inf. Sci. Technol.}, publisher = {John Wiley & Sons, Inc.}, address = {New York, NY, USA}, year = {2010}, volume = {61}, number = {1}, pages = {150--168}, url = {http://portal.acm.org/citation.cfm?id=1672957.1672980&coll=portal&dl=ACM}, doi = {http://dx.doi.org/10.1002/asi.v61:1}, issn = {1532-2882}, keywords = {ol}, abstract = {Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.} }

%0 = article %A = Jiang, Xing and Tan, Ah-Hwee %C = New York, NY, USA %D = 2010 %I = John Wiley & Sons, Inc. %T = CRCTOL: A semantic-based domain ontology learning system %U = http://portal.acm.org/citation.cfm?id=1672957.1672980&coll=portal&dl=ACM

BACKGROUND: Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. RESULTS: Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/iffer/animalbehaviour/. CONCLUSION: We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

@article{Brewster:2009:BMC-Bioinformatics:19426458, author = {Brewster, C and Jupp, S and Luciano, J and Shotton, D and Stevens, R D and Zhang, Z}, title = {Issues in learning an ontology from text}, journal = {BMC Bioinformatics}, year = {2009}, volume = {10 Suppl 5}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19426458}, doi = {10.1186/1471-2105-10-S5-S1}, keywords = {learning, ol, ontology}, abstract = {BACKGROUND: Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. RESULTS: Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/iffer/animalbehaviour/. CONCLUSION: We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.} }

%0 = article %A = Brewster, C and Jupp, S and Luciano, J and Shotton, D and Stevens, R D and Zhang, Z %D = 2009 %T = Issues in learning an ontology from text %U = http://www.ncbi.nlm.nih.gov/pubmed/19426458

In this paper we argue why it is necessary to associate linguistic information with ontologies and why more expressive models, beyond RDFS, OWL and SKOS, are needed to capture the relation between natural language constructs on the one hand and ontological entities on the other. We argue that in the light of tasks such as ontology-based information extraction, ontology learning and population from text and natural language generation from ontologies, currently available datamodels are not sufficient as they only allow to associate atomic terms without linguistic grounding or structure to ontology elements. Towards realizing a more expressive model for associating linguistic information to ontology elements, we base our work presented here on previously developed models (LingInfo, LexOnto, LMF) and present a new joint model for linguistic grounding of ontologies called LexInfo. LexInfo combines essential design aspects of LingInfo and LexOnto and builds on a sound model for representing computational lexica called LMF which has been recently approved as a standard under ISO.

@inproceedings{linguistically2009, author = {Buitelaar, Paul and Cimiano, Philipp and Haase, Peter and Sintek, Michael}, title = {Towards Linguistically Grounded Ontologies}, booktitle = {6th Annual European Semantic Web Conference (ESWC2009)}, year = {2009}, pages = {111-125}, url = {http://www.cimiano.de/Publications/2009/eswc09/eswc09.pdf}, keywords = {nlp2rdf, ol, ontology}, abstract = {In this paper we argue why it is necessary to associate linguistic information with ontologies and why more expressive models, beyond RDFS, OWL and SKOS, are needed to capture the relation between natural language constructs on the one hand and ontological entities on the other. We argue that in the light of tasks such as ontology-based information extraction, ontology learning and population from text and natural language generation from ontologies, currently available datamodels are not sufficient as they only allow to associate atomic terms without linguistic grounding or structure to ontology elements. Towards realizing a more expressive model for associating linguistic information to ontology elements, we base our work presented here on previously developed models (LingInfo, LexOnto, LMF) and present a new joint model for linguistic grounding of ontologies called LexInfo. LexInfo combines essential design aspects of LingInfo and LexOnto and builds on a sound model for representing computational lexica called LMF which has been recently approved as a standard under ISO.} }

%0 = inproceedings %A = Buitelaar, Paul and Cimiano, Philipp and Haase, Peter and Sintek, Michael %B = 6th Annual European Semantic Web Conference (ESWC2009) %D = 2009 %T = Towards Linguistically Grounded Ontologies %U = http://www.cimiano.de/Publications/2009/eswc09/eswc09.pdf

@article{journals/www/EdaYUU09, author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu}, title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.}, journal = {World Wide Web}, year = {2009}, volume = {12}, number = {4}, pages = {421-440}, url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09}, keywords = {analysis, folksonomy, ol, semantic, taxonomy, toread} }

%0 = article %A = Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu %D = 2009 %T = The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags. %U = http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09

Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.

@article{Hazman:30May2009:1744-2621:24, author = {Hazman, Maryam and El-Beltagy, Samhaa R. and Rafea, Ahmed}, title = {Ontology learning from domain specific web documents}, journal = {International Journal of Metadata, Semantics and Ontologies}, year = {2009}, volume = {4}, pages = {24-33(10)}, url = {http://www.ingentaconnect.com/content/ind/ijmso/2009/00000004/F0020001/art00003}, doi = {doi:10.1504/IJMSO.2009.026251}, keywords = {learning, ol, ontology}, abstract = {Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.} }

%0 = article %A = Hazman, Maryam and El-Beltagy, Samhaa R. and Rafea, Ahmed %D = 2009 %T = Ontology learning from domain specific web documents %U = http://www.ingentaconnect.com/content/ind/ijmso/2009/00000004/F0020001/art00003

Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.

@inproceedings{plangprasopchok2009, author = {Plangprasopchok, A. and Lerman, K.}, title = {Constructing folksonomies from user-specified relations on flickr}, booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {781--790}, url = {http://www2009.org/proceedings/pdf/p781.pdf}, doi = {http://doi.acm.org/10.1145/1526709.1526814}, isbn = {978-1-60558-487-4}, keywords = {folksonomy, learning, ol, relation, tagging, taggingsurvey}, abstract = {Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.} }

%0 = inproceedings %A = Plangprasopchok, A. and Lerman, K. %B = WWW '09: Proceedings of the 18th international conference on World wide web %C = New York, NY, USA %D = 2009 %I = ACM %T = Constructing folksonomies from user-specified relations on flickr %U = http://www2009.org/proceedings/pdf/p781.pdf

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.

@inproceedings{5273871, author = {Silva, L. De and Jayaratne, L.}, title = {Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus}, booktitle = {Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the}, year = {2009}, pages = {446-451}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116}, doi = {10.1109/ICADIWT.2009.5273871}, keywords = {learning, nlp, ol, ontology, wikipedia}, 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.} }

%0 = inproceedings %A = Silva, L. De and Jayaratne, L. %B = Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the %D = 2009 %T = Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus %U = http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116

@phdthesis{Brewster_Knowledge08, author = {Brewster, Christopher}, title = {Mind the Gap: Bridging from Text to Ontological Knowledge}, school = {Department of Computer Science, University of Sheffield}, year = {2008}, keywords = {knowledge, ol, ontology} }

%0 = phdthesis %A = Brewster, Christopher %D = 2008 %T = Mind the Gap: Bridging from Text to Ontological Knowledge

@inproceedings{conf/ecai/HjelmB08, author = {Hjelm, Hans and Buitelaar, Paul}, title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.}, editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.}, booktitle = {ECAI}, series = {Frontiers in Artificial Intelligence and Applications}, publisher = {IOS Press}, year = {2008}, volume = {178}, pages = {288-292}, url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf}, isbn = {978-1-58603-891-5}, keywords = {antrag, learning, multilingual, ol, ontology} }

%0 = inproceedings %A = Hjelm, Hans and Buitelaar, Paul %B = ECAI %D = 2008 %I = IOS Press %T = Multilingual Evidence Improves Clustering-based Taxonomy Extraction. %U = http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf

@inproceedings{sorg2008cirwesa, author = {Sorg, Philipp and Cimiano, Philipp}, title = {Cross-lingual Information Retrieval with Explicit Semantic Analysis}, booktitle = {Working Notes for the CLEF 2008 Workshop}, type = {Inproceedings}, year = {2008}, url = {http://www.aifb.kit.edu/images/7/7c/2008_1837_Sorg_Cross-lingual_I_1.pdf}, keywords = {cross, information, lingual, ol, ontology} }

%0 = inproceedings %A = Sorg, Philipp and Cimiano, Philipp %B = Working Notes for the CLEF 2008 Workshop %D = 2008 %T = Cross-lingual Information Retrieval with Explicit Semantic Analysis %U = http://www.aifb.kit.edu/images/7/7c/2008_1837_Sorg_Cross-lingual_I_1.pdf

@incollection{Voelker2008, author = {Völker, J. and Haase, P. and Hitzler, P.}, title = {Learning Expressive Ontologies}, booktitle = {Ontology Learning and Population: Bridging the Gap between Text and Knowledge}, series = {Frontiers in Artificial Intelligence and Applications}, publisher = {IOS Press}, year = {2008}, keywords = {axiom, learning, ol, ontology} }

%0 = incollection %A = Völker, J. and Haase, P. and Hitzler, P. %B = Ontology Learning and Population: Bridging the Gap between Text and Knowledge %D = 2008 %I = IOS Press %T = Learning Expressive Ontologies

@inproceedings{Van-Damme2007, author = {Damme, Céline Van and Hepp, Martin and Siorpaes, Katharina}, title = {FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies}, booktitle = {Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007)}, address = {Innsbruck}, year = {2007}, pages = {57--70}, url = {http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/ProceedingsSemnet07.pdf}, keywords = {approach, folksonomies, integrated, ol, ontologies, tagging, taggingsurvey, toread, turning} }

%0 = inproceedings %A = Damme, Céline Van and Hepp, Martin and Siorpaes, Katharina %B = Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007) %C = Innsbruck %D = 2007 %T = FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies %U = http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/ProceedingsSemnet07.pdf

@inproceedings{conf/icail/LenciMPV07, author = {Lenci, Alessandro and Montemagni, Simonetta and Pirrelli, Vito and Venturi, Giulia}, title = {NLP-based Ontology Learning from Legal Texts. A Case Study.}, editor = {Casanovas, Pompeu and Biasiotti, Maria Angela and Francesconi, Enrico and Sagri, Maria-Teresa}, booktitle = {LOAIT}, series = {CEUR Workshop Proceedings}, publisher = {CEUR-WS.org}, year = {2007}, volume = {321}, pages = {113-129}, url = {http://dblp.uni-trier.de/db/conf/icail/loait2007.html#LenciMPV07}, keywords = {learning, ol, ontology} }

%0 = inproceedings %A = Lenci, Alessandro and Montemagni, Simonetta and Pirrelli, Vito and Venturi, Giulia %B = LOAIT %D = 2007 %I = CEUR-WS.org %T = NLP-based Ontology Learning from Legal Texts. A Case Study. %U = http://dblp.uni-trier.de/db/conf/icail/loait2007.html#LenciMPV07

@phdthesis{david2007domain, author = {Sánchez, David}, title = {Domain ontology learning from the web an unsupervised, automatic and domain independent approach}, publisher = {VDM Verlag Dr. Müller}, address = {Saarbrücken}, year = {2007}, pages = {--}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=3836470691}, isbn = {9783836470698 3836470691}, keywords = {learning, ol, ontology} }

%0 = phdthesis %A = Sánchez, David %C = Saarbrücken %D = 2007 %I = VDM Verlag Dr. Müller %T = Domain ontology learning from the web an unsupervised, automatic and domain independent approach %U = http://www.worldcat.org/search?qt=worldcat_org_all&q=3836470691

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.

@inproceedings{Zhou/2007/Unsupervised, author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong}, title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations}, 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}, booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea}, series = {LNCS}, publisher = {Springer Verlag}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4825}, pages = {673--686}, url = {http://iswc2007.semanticweb.org/papers/673.pdf}, keywords = {folksonomy, ol, semantic, tagging, taggingsurvey}, 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.} }

%0 = inproceedings %A = Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong %B = Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea %C = Berlin, Heidelberg %D = 2007 %I = Springer Verlag %T = An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations %U = http://iswc2007.semanticweb.org/papers/673.pdf

@article{ieKey, author = {Cimiano, Philipp and V"olker, Johanna and Studer, Rudi}, title = {Ontologies on Demand? -A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text Information}, journal = {Information, Wissenschaft und Praxis}, year = {2006}, volume = {57}, number = {6-7}, pages = {315-320}, url = {http://www.aifb.uni-karlsruhe.de/Publikationen/showPublikation?publ_id=1282}, keywords = {learning, ol, ontology, survey} }

%0 = article %A = Cimiano, Philipp and V"olker, Johanna and Studer, Rudi %D = 2006 %T = Ontologies on Demand? -A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text Information %U = http://www.aifb.uni-karlsruhe.de/Publikationen/showPublikation?publ_id=1282

@inproceedings{brank2005, author = {Brank, Janez and Grobelnik, Marko and Mladenić, Dunja}, title = {A Survey of Ontology Evaluation Techniques}, booktitle = {Proc. of 8th Int. multi-conf. Information Society}, year = {2005}, pages = {166--169}, keywords = {evaluation, ol, ontology, survey} }

%0 = inproceedings %A = Brank, Janez and Grobelnik, Marko and Mladenić, Dunja %B = Proc. of 8th Int. multi-conf. Information Society %D = 2005 %T = A Survey of Ontology Evaluation Techniques

Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.

@article{1179190, author = {Navigli, R. and Velardi, P. and Gangemi, A.}, title = {Ontology learning and its application to automated terminology translation}, journal = {Intelligent Systems, IEEE}, year = {2003}, volume = {18}, number = {1}, pages = { 22-31}, url = {http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1179190}, doi = {10.1109/MIS.2003.1179190}, issn = {1541-1672}, keywords = {learning, ol, ontology}, abstract = { Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.} }

%0 = article %A = Navigli, R. and Velardi, P. and Gangemi, A. %D = 2003 %T = Ontology learning and its application to automated terminology translation %U = http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1179190

@inproceedings{666125, author = {Brewster, Christopher and Ciravegna, Fabio and Wilks, Yorick}, title = {User-Centred Ontology Learning for Knowledge Management}, booktitle = {NLDB '02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers}, publisher = {Springer-Verlag}, address = {London, UK}, year = {2002}, pages = {203--207}, url = {http://portal.acm.org/citation.cfm?id=666125}, isbn = {3-540-00307-X}, keywords = {learning, ol, ontology} }

%0 = inproceedings %A = Brewster, Christopher and Ciravegna, Fabio and Wilks, Yorick %B = NLDB '02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers %C = London, UK %D = 2002 %I = Springer-Verlag %T = User-Centred Ontology Learning for Knowledge Management %U = http://portal.acm.org/citation.cfm?id=666125