JabRef References output

Author	Title	Year	Journal/Proceedings	Reftype	DOI/URL
Strohmaier, M., Helic, D., Benz, D., Körner, C. & Kern, R.	Evaluation of Folksonomy Induction Algorithms [BibTeX]	2012	Transactions on Intelligent Systems and Technology	article	URL
BibTeX: @article{strohmaier2011evaluation, author = {Strohmaier, Markus and Helic, Denis and Benz, Dominik and Körner, Christian and Kern, Roman}, title = {Evaluation of Folksonomy Induction Algorithms}, journal = {Transactions on Intelligent Systems and Technology}, year = {2012}, url = {http://tist.acm.org/index.html} }
Garcia-Silva, A., Corcho, O., Alani, H. & Gomez-Perez, A.	Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies [Abstract] [BibTeX]	2011	Knowledge Engineering Review Vol. 26(4)	article
Abstract: This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.
BibTeX: @article{garciasilva2011review, author = {Garcia-Silva, Andres and Corcho, Oscar and Alani, Harith and Gomez-Perez, Asuncion}, title = {Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies}, journal = {Knowledge Engineering Review}, year = {2011}, volume = {26}, number = {4} }
Weichselbraun, A.	Ontology Learning based on Text Mining and Social Evidence Sources [BibTeX]	2011		unpublished	URL
BibTeX: @unpublished{weichselbraun2011ontology, author = {Weichselbraun, Albert}, title = {Ontology Learning based on Text Mining and Social Evidence Sources}, year = {2011}, note = {Presentation Slides only}, url = {http://eprints.weblyzard.com/27/} }
Ireson, N. & Ciravegna, F.	Toponym Resolution in Social Media. [Abstract] [BibTeX]	2010	Vol. 6496#iswc2010#, pp. 370-385	inproceedings	URL
Abstract: Increasingly user-generated content is being utilised as a source of information, however each individual piece of content tends to contain low levels of information. In addition, such information tends to be informal and imperfect in nature; containing imprecise, subjective, ambiguous expressions. However the content does not have to be interpreted in isolation as it is linked, either explicitly or implicitly, to a network of interrelated content; it may be grouped or tagged with similar content, comments may be added by other users or it may be related to other content posted at the same time or by the same author or members of the author's social network. This paper generally examines how ambiguous concepts within user-generated content can be assigned a specific/formal meaning by considering the expanding context of the information, i.e. other information contained within directly or indirectly related content, and specifically considers the issue of toponym resolution of locations.
BibTeX: @inproceedings{ireson2010toponym, author = {Ireson, Neil and Ciravegna, Fabio}, title = {Toponym Resolution in Social Media.}, booktitle = {#iswc2010#}, publisher = {Springer}, year = {2010}, volume = {6496}, pages = {370-385}, url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#IresonC10} }
Keller, C.	Theoretical and Practical Perspectives on Ontology Learning from Folksonomies [BibTeX]	2010	School: Universität Stuttgart	mastersthesis
BibTeX: @mastersthesis{keller2010theoretical, author = {Keller, Christine}, title = {Theoretical and Practical Perspectives on Ontology Learning from Folksonomies}, school = {Universität Stuttgart}, year = {2010} }
Meder, M.	Multi-Domain Klassifikation basierend auf nutzergenerierten Metadaten [BibTeX]	2010	School: Technische Universität Berlin	mastersthesis
BibTeX: @mastersthesis{meder2010multidomain, author = {Meder, Michael}, title = {Multi-Domain Klassifikation basierend auf nutzergenerierten Metadaten}, school = {Technische Universität Berlin}, year = {2010} }
Cimiano, P., Mädche, A., Staab, S. & Völker, J.	Ontology Learning [BibTeX]	2009	Handbook on Ontologies, pp. 245-267	incollection	URL
BibTeX: @incollection{cimiano2009ontology, author = {Cimiano, Philipp and Mädche, Alexander and Staab, Steffen and Völker, Johanna}, title = {Ontology Learning}, booktitle = {Handbook on Ontologies}, publisher = {Springer Berlin Heidelberg}, year = {2009}, pages = {245-267}, url = {http://dx.doi.org/10.1007/978-3-540-92673-3_11} }
Garcia, A., Szomszor, M., Alani, H. & Corcho, O.	Preliminary Results in Tag Disambiguation using DBpedia [Abstract] [BibTeX]	2009	Knowledge Capture (K-Cap'09) - First International Workshop on Collective Knowledge Capturing and Representation - CKCaR'09	inproceedings	URL
Abstract: The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area.
BibTeX: @inproceedings{garcia2009preliminary, author = {Garcia, Andres and Szomszor, Martin and Alani, Harith and Corcho, Oscar}, title = {Preliminary Results in Tag Disambiguation using DBpedia}, booktitle = {Knowledge Capture (K-Cap'09) - First International Workshop on Collective Knowledge Capturing and Representation - CKCaR'09}, year = {2009}, url = {http://eprints.ecs.soton.ac.uk/17792/} }
Hazman, M., El-Beltagy, S.R. & Rafea, A.	Ontology learning from domain specific web documents [Abstract] [BibTeX]	2009	International Journal of Metadata, Semantics and Ontologies Vol. 4, pp. 24-33(10)	article	DOI URL
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.
BibTeX: @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 = {http://dx.doi.org/10.1504/IJMSO.2009.026251} }
Angeletou, S., Sabou, M. & Motta, E.	Semantically enriching folksonomies with FLOR [Abstract] [BibTeX]	2008	Proceedings of the CISWeb Workshop, located at the 5th European Semantic Web Conference ESWC 2008	inproceedings	URL
Abstract: Abstract. While the increasing popularity of folksonomies has lead to a vast quantity of tagged data, resource retrieval in folksonomies is limited by being agnostic to the meaning (i.e., semantics) of tags. Our goal is to automatically enrich folksonomy tags (and implicitly the related resources) with formal semantics by associating them to relevant concepts defined in online ontologies. We introduce FLOR, a method that performs automatic folksonomy enrichment by combining knowledge from WordNet and online available ontologies. Experimentally testing FLOR, we found that it correctly enriched 72 % of 250 Flickr photos. 1
BibTeX: @inproceedings{angeletou2008semantically, author = {Angeletou, Sofia and Sabou, Marta and Motta, Enrico}, title = {Semantically enriching folksonomies with FLOR}, booktitle = {Proceedings of the CISWeb Workshop, located at the 5th European Semantic Web Conference ESWC 2008}, year = {2008}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2569} }
Hjelm, H. & Buitelaar, P.	Multilingual Evidence Improves Clustering-based Taxonomy Extraction. [Abstract] [BibTeX]	2008	Vol. 178ECAI, pp. 288-292	inproceedings	URL
Abstract: We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clustering based on distributional similarity of the terms in texts. We show that basing the clustering on a comparable corpus in four languages gives a considerable improvement in accuracy compared to using only the monolingual English texts. We also show that hierarchical k-means clustering increases the similarity to the original taxonomy, when compared with a bottom-up agglomerative clustering approach.
BibTeX: @inproceedings{hjelm2008multilingual, author = {Hjelm, Hans and Buitelaar, Paul}, title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.}, booktitle = {ECAI}, publisher = {IOS Press}, year = {2008}, volume = {178}, pages = {288-292}, url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf} }
Tesconi, M., Ronzano, F., Marchetti, A. & Minutoli, S.	Semantify del.icio.us: Automatically Turn your Tags into Senses [Abstract] [BibTeX]	2008	Proceedings of the Workshop Social Data on the Web (SDoW2008)	inproceedings	URL
Abstract: At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. 1
BibTeX: @inproceedings{tesconi2008semantify, author = {Tesconi, Maurizio and Ronzano, Francesco and Marchetti, Andrea and Minutoli, Salvatore}, title = {Semantify del.icio.us: Automatically Turn your Tags into Senses}, booktitle = {Proceedings of the Workshop Social Data on the Web (SDoW2008)}, year = {2008}, url = {http://CEUR-WS.org/Vol-405/paper8.pdf} }
Auer, Sö. & Lehmann, J.	What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content [Abstract] [BibTeX]	2007	ESWC, pp. 503-517	inproceedings	URL
Abstract: Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used.
BibTeX: @inproceedings{auer2007what, author = {Auer, Sören and Lehmann, Jens}, title = {What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content}, booktitle = {ESWC}, year = {2007}, pages = {503-517}, url = {http://www.springerlink.com/content/3131t21p634191n2/} }
Baeza-Yates, R. & Tiberi, A.	Extracting semantic relations from query logs [Abstract] [BibTeX]	2007	KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 76-85	inproceedings	DOI URL
Abstract: In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.
BibTeX: @inproceedings{baezayates2007extracting, author = {Baeza-Yates, Ricardo and Tiberi, Alessandro}, title = {Extracting semantic relations from query logs}, booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining}, publisher = {ACM}, year = {2007}, pages = {76--85}, url = {http://portal.acm.org/citation.cfm?id=1281192.1281204}, doi = {http://doi.acm.org/10.1145/1281192.1281204} }
Kennedy, L., Naaman, M., Ahern, S., Nair, R. & Rattenbury, T.	How flickr helps us make sense of the world: context and content in community-contributed media collections [Abstract] [BibTeX]	2007	MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia, pp. 631-640	inproceedings	DOI URL
Abstract: The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities – and new challenges – to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate “knowledge�?? As a test case, we study Flickr – a popular photo sharing website. Flickr supports photo, time and location metadata, as well as a light-weight annotation model. We extract information from this dataset using two different approaches. First, we employ a location-driven approach to generate aggregate knowledge in the form of “representative tags�? for arbitrary areas in the world. Second, we use a tag-driven approach to automatically extract place and event semantics for Flickr tags, based on each tag’s metadata patterns. With the patterns we extract from tags and metadata, vision algorithms can be employed with greater precision. In particular, we demonstrate a location-tag-vision-based approach to retrieving images of geography-related landmarks and features from the Flickr dataset. The results suggest that community-contributed media and annotation can enhance and improve our access to multimedia resources – and our understanding of the world.
BibTeX: @inproceedings{kennedy2007how, author = {Kennedy, Lyndon and Naaman, Mor and Ahern, Shane and Nair, Rahul and Rattenbury, Tye}, title = {How flickr helps us make sense of the world: context and content in community-contributed media collections}, booktitle = {MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia}, publisher = {ACM}, year = {2007}, pages = {631--640}, url = {http://dx.doi.org/10.1145/1291233.1291384}, doi = {http://dx.doi.org/10.1145/1291233.1291384} }
Cimiano, P.	Ontology learning and population from text - algorithms, evaluation and applications. [BibTeX]	2006	, pp. I-XXVIII, 1-347	book
BibTeX: @book{cimiano2006ontology, author = {Cimiano, Philipp}, title = {Ontology learning and population from text - algorithms, evaluation and applications.}, publisher = {Springer}, year = {2006}, pages = {I-XXVIII, 1-347} }
Brank, J., Grobelnik, M. & Mladenić, D.	A Survey of Ontology Evaluation Techniques [Abstract] [BibTeX]	2005	Proc. of 8th Int. multi-conf. Information Society, pp. 166-169	inproceedings
Abstract: An ontology is an explicit formal conceptualization of some domain of interest. Ontologies are increasingly used in various fields such as knowledge management, information extraction, and the semantic web. Ontology evaluation is the problem of assessing a given ontology from the point of view of a particular criterion of application, typically in order to determine which of several ontologies would best suit a particular purpose. This paper presents a survey of the state of the art in ontology evaluation.
BibTeX: @inproceedings{brank2005survey, 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} }
Cimiano, P., Staab, S. & Tane, J.	Automatic Acquisition of Taxonomies from Text: FCA meets NLP [Abstract] [BibTeX]	2003	Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining, pp. 10-17	inproceedings	URL
Abstract: We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from domain-specific texts based on Formal Concept Analysis (FCA). Our approach is based on the assumption that verbs pose more or less strong selectional restrictions on their arguments. The conceptual hierarchy is then built on the basis of the inclusion relations between the extensions of the selectional restrictions of all the verbs, while the verbs themselves provide intensional descriptions for each concept. We formalize this idea in terms of FCA and show how our approach can be used to acquire a concept hierarchy for the tourism domain out of texts. We then evaluate our method by considering an already existing ontology for this domain.
BibTeX: @inproceedings{cimiano2003automatic, author = {Cimiano, Philipp and Staab, Steffen and Tane, Julien}, title = {Automatic Acquisition of Taxonomies from Text: FCA meets NLP}, booktitle = {Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining}, year = {2003}, pages = {10--17}, url = {http://www.dcs.shef.ac.uk/~fabio/ATEM03/cimiano-ecml03-atem.pdf} }
Widdows, D. & Dorow, B.	A Graph Model for Unsupervised Lexical Acquisition [BibTeX]	2002	COLING	inproceedings
BibTeX: @inproceedings{widdows2002graph, author = {Widdows, Dominic and Dorow, Beate}, title = {A Graph Model for Unsupervised Lexical Acquisition}, booktitle = {COLING}, year = {2002} }
Studer, R., Benjamins, R.R. & Fensel, D.	Knowledge Engineering: Principles and Methods [Abstract] [BibTeX]	1998	Data Knowledge Engineering Vol. 25(1-2), pp. 161-197	article	URL
Abstract: This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÃ‰GÃ‰-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.
BibTeX: @article{studer1998knowledge, author = {Studer, Rudi and Benjamins, Richard R. and Fensel, Dieter}, title = {Knowledge Engineering: Principles and Methods}, journal = {Data Knowledge Engineering}, year = {1998}, volume = {25}, number = {1-2}, pages = {161--197}, url = {http://www.cs.toronto.edu/~nernst/papers/studer98knowledge.pdf} }

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