@incollection{pol_introduction,
author = {Lehmann, Jens and Voelker, Johanna},
booktitle = {Perspectives on Ontology Learning},
editor = {Lehmann, Jens and Voelker, Johanna},
interhash = {a53a9f1796f71f2f1c5ec646961f8924},
intrahash = {cf6a6785f5cab0525632a003c47ef5f7},
owner = {jl},
pages = {ix-xvi},
publisher = {AKA / IOS Press},
title = {An Introduction to Ontology Learning},
url = {http://jens-lehmann.org/files/2014/pol_introduction.pdf},
year = 2014
}
@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
}
@book{staab2009handbook,
abstract = {An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. This book considers ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications.},
address = {Berlin},
author = {Staab, Steffen and Studer, Rudi},
interhash = {c2e7c401bef2cee2bb8b12334d3c7a88},
intrahash = {be122d99dc6dd20cb58a55d62d8eca6c},
isbn = {9783540926733 3540926739},
publisher = {Springer},
refid = {569892085},
title = {Handbook on ontologies},
url = {http://public.eblib.com/choice/publicfullrecord.aspx?p=571805},
year = 2009
}
@book{pan2013ontologydriven,
address = {Berlin [u.a.]},
editor = {Pan, Jeff Z.},
format = {book},
interhash = {b227c90d573b8bbe06380a07d797612e},
intrahash = {b88adb2114769033172f3974ad1aaaac},
isbn = {9783642312250},
partauthors = {Pan, Jeff Z.
Pan, Jeff Z. (Hrsg.)},
publisher = {Springer},
shorttitle = {Ontology-Driven Software Development},
subtitle = {Jeff Z. Pan ... eds.},
title = {Ontology-Driven Software Development},
titlestatement = {Jeff Z. Pan ... eds.},
uniqueid = {HEB309548594},
url = {http://scans.hebis.de/HEBCGI/show.pl?30954859_cov.jpg},
year = 2013
}
@inproceedings{suchanek2007semantic,
abstract = {We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.},
acmid = {1242667},
address = {New York, NY, USA},
author = {Suchanek, Fabian M. and Kasneci, Gjergji and Weikum, Gerhard},
booktitle = {Proceedings of the 16th international conference on World Wide Web},
doi = {10.1145/1242572.1242667},
interhash = {1d2c2b23ce2a6754d12c4364e19c574c},
intrahash = {84ae693c0a6dfb6d4b051b0b6dbd3668},
isbn = {978-1-59593-654-7},
location = {Banff, Alberta, Canada},
numpages = {10},
pages = {697--706},
publisher = {ACM},
title = {YAGO: a core of semantic knowledge},
url = {http://doi.acm.org/10.1145/1242572.1242667},
year = 2007
}
@inproceedings{baader2007completing,
abstract = {We propose an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using information provided by the knowledge base and by a domain expert. The use of techniques from Formal Concept Analysis ensures that, on the one hand, the interaction with the expert is kept to a minimum, and, on the other hand, we can show that the extended knowledge base is complete in a certain, well-defined sense.},
acmid = {1625311},
address = {San Francisco, CA, USA},
author = {Baader, Franz and Ganter, Bernhard and Sertkaya, Baris and Sattler, Ulrike},
booktitle = {Proceedings of the 20th international joint conference on Artifical intelligence},
interhash = {8ab382f3aa141674412ba7ad33316a9b},
intrahash = {87f98ae486014ba78690ffa314b67da8},
location = {Hyderabad, India},
numpages = {6},
pages = {230--235},
publisher = {Morgan Kaufmann Publishers Inc.},
title = {Completing description logic knowledge bases using formal concept analysis},
url = {http://dl.acm.org/citation.cfm?id=1625275.1625311},
year = 2007
}
@inproceedings{hearst1992automatic,
abstract = {We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.},
acmid = {992154},
address = {Stroudsburg, PA, USA},
author = {Hearst, Marti A.},
booktitle = {Proceedings of the 14th conference on Computational linguistics},
doi = {10.3115/992133.992154},
interhash = {8c1e90c6cc76625c34f20370a1af7ea2},
intrahash = {2c49ad19ac6977bd806b6687e4dcc550},
location = {Nantes, France},
numpages = {7},
pages = {539--545},
publisher = {Association for Computational Linguistics},
title = {Automatic acquisition of hyponyms from large text corpora},
url = {http://dx.doi.org/10.3115/992133.992154},
volume = 2,
year = 1992
}
@article{noy2004ontology,
abstract = {As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.},
address = {London},
affiliation = {Stanford Medical Informatics Stanford University Stanford CA 94305 USA},
author = {Noy, Natalya F. and Klein, Michel},
doi = {10.1007/s10115-003-0137-2},
interhash = {4b4ee2090ba5356a3d0e853192968662},
intrahash = {08ee0381e240c3ee414e0eefc7fe1a83},
issn = {0219-1377},
journal = {Knowledge and Information Systems},
keyword = {Computer Science},
number = 4,
pages = {428--440},
publisher = {Springer},
title = {Ontology Evolution: Not the Same as Schema Evolution},
url = {http://dx.doi.org/10.1007/s10115-003-0137-2},
volume = 6,
year = 2004
}
@inproceedings{daquin2011extracting,
abstract = {With the rise of linked data, more and more semantically described information is being published online according to the principles and technologies of the Semantic Web (especially, RDF and SPARQL). The use of such standard technologies means that this data should be exploitable, integrable and reusable straight away. However, once a potentially interesting dataset has been discovered, significant efforts are currently required in order to understand its schema, its content, the way to query it and what it can answer. In this paper, we propose a method and a tool to automatically discover questions that can be answered by an RDF dataset. We use formal concept analysis to build a hierarchy of meaningful sets of entities from a dataset. These sets of entities represent answers, which common characteristics represent the clauses of the corresponding questions. This hierarchy can then be used as a querying interface, proposing questions of varying levels of granularity and specificity to the user. A major issue is however that thousands of questions can be included in this hierarchy. Based on an empirical analysis and using metrics inspired both from formal concept analysis and from ontology summarization, we devise an approach for identifying relevant questions to act as a starting point to the navigation in the question hierarchy.},
acmid = {1999698},
address = {New York, NY, USA},
author = {d'Aquin, Mathieu and Motta, Enrico},
booktitle = {Proceedings of the sixth international conference on Knowledge capture},
doi = {10.1145/1999676.1999698},
interhash = {7794150f2b42c21956eb7fb419ca0248},
intrahash = {45374b975834248c0cd87022fc854e25},
isbn = {978-1-4503-0396-5},
location = {Banff, Alberta, Canada},
numpages = {8},
pages = {121--128},
publisher = {ACM},
title = {Extracting relevant questions to an RDF dataset using formal concept analysis},
url = {http://doi.acm.org/10.1145/1999676.1999698},
year = 2011
}
@inproceedings{conf/dagstuhl/Stumme05,
author = {Stumme, Gerd},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {Semantic Interoperability and Integration},
editor = {Kalfoglou, Yannis and Schorlemmer, W. Marco and Sheth, Amit P. and Staab, Steffen and Uschold, Michael},
ee = {http://drops.dagstuhl.de/opus/volltexte/2005/49},
interhash = {9206884ea0e91905062366300cfc4870},
intrahash = {225d908cff3ee338f7595032f236fd07},
publisher = {IBFI, Schloss Dagstuhl, Germany},
series = {Dagstuhl Seminar Proceedings},
title = {Ontology Merging with Formal Concept Analysis},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/stumme2005ontology.pdf},
volume = 04391,
year = 2005
}
@inproceedings{schmitz2006content,
abstract = {Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.
This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.},
address = {Heidelberg},
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
booktitle = {The Semantic Web: Research and Applications},
editor = {Sure, York and Domingue, John},
interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
intrahash = {1788c88e04112a4491f19dfffb8dc39e},
pages = {530-544},
publisher = {Springer},
series = {LNAI},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
volume = 4011,
year = 2006
}
@phdthesis{Reichle2010,
abstract = {Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.},
address = {Wilhelmshöher Allee 73, 34121 Kassel, Germany},
author = {Reichle, Roland},
interhash = {9ab2a238086ed25d916f14df296ff3b8},
intrahash = {3c9894e41906dc36d2e286c40d197bf8},
month = {dez},
school = {University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, Distributed Systems Group},
title = {Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments},
url = {http://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2010121035166},
year = 2010
}
@incollection{solskinnsbakk2010hybrid,
abstract = {Folksonomies are becoming increasingly popular. They contain large amounts of data which can be mined and utilized for many tasks like visualization, browsing, information retrieval etc. An inherent problem of folksonomies is the lack of structure. In this paper we present an unsupervised approach for generating such structure based on a combination of association rule mining and the underlying tagged material. Using the underlying tagged material we generate a semantic representation of each tag. The semantic representation of the tags is an integral component of the structure generated. The experiment presented in this paper shows promising results with tag structures that correspond well with human judgment.},
address = {Berlin / Heidelberg},
affiliation = {Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway},
author = {Solskinnsbakk, Geir and Gulla, Jon},
booktitle = {On the Move to Meaningful Internet Systems, OTM 2010},
doi = {10.1007/978-3-642-16949-6_22},
editor = {Meersman, Robert and Dillon, Tharam and Herrero, Pilar},
interhash = {c33c0fe08d8ac29e88a4c43b3047c707},
intrahash = {949d497bc5a29eda10c77f5784aed18b},
isbn = {978-3-642-16948-9},
keyword = {Computer Science},
pages = {975-982},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
slides = {http://www.slides.com},
title = {A Hybrid Approach to Constructing Tag Hierarchies},
url = {http://dx.doi.org/10.1007/978-3-642-16949-6_22},
volume = 6427,
year = 2010
}
@inproceedings{plangprasopchok2010probabilistic,
abstract = {Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering. },
author = {Plangprasopchok, Anon and Lerman, Kristina and Getoor, Lise},
booktitle = {Proceedings of the 4th ACM Web Search and Data Mining Conference},
interhash = {826359ec25dcd228ad3ef46dcc6d26c5},
intrahash = {455bb173bb33af58bc8aaed48d8a8513},
note = {cite arxiv:1011.3557Comment: In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)},
title = {A Probabilistic Approach for Learning Folksonomies from Structured Data},
url = {http://arxiv.org/abs/1011.3557},
year = 2010
}
@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
}
@incollection{hoser2006semantic,
abstract = { A key argument for modeling knowledge in ontologies is the easy reuse and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA).While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size. },
address = {Berlin/Heidelberg},
author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
booktitle = {The Semantic Web: Research and Applications},
doi = {10.1007/11762256_38},
editor = {Sure, York and Domingue, John},
interhash = {344ec3b4ee8af1a2c6b86efc14917fa9},
intrahash = {2b720233e4493d4e0dee95be86dd07e8},
isbn = {978-3-540-34544-2},
note = {10.1007/11762256_38},
pages = {514--529},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Semantic Network Analysis of Ontologies},
url = {http://dx.doi.org/10.1007/11762256_38},
volume = 4011,
year = 2006
}
@inproceedings{elsenbroich2006abductive,
abstract = {We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description logics.},
author = {Elsenbroich, Corinna and Kutz, Oliver and Sattler, Ulrike},
booktitle = {Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions},
editor = {Grau, Bernardo Cuenca and Hitzler, Pascal and Shankey, Conor and Wallace, Evan},
interhash = {a5936835f9eeab91eb09d84948306178},
intrahash = {15a1bdcbff44431651957f45097dc4f4},
issn = {1613-0073},
month = nov,
series = {CEUR-WS.org},
title = {A case for abductive reasoning over ontologies},
url = {http://www.cs.man.ac.uk/~okutz/case-for-abduction.pdf},
volume = 216,
year = 2006
}
@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
}
@article{gasevic2011approach,
address = {Los Alamitos, CA, USA},
author = {Gasevic, Dragan and Zouaq, Amal and Torniai, Carlo and Jovanovic, Jelena and Hatala, Marek},
doi = {10.1109/TLT.2011.21},
interhash = {58ca3b2f09e3962d17da8755b5b07ac0},
intrahash = {b701b92c234afa36aac87635f687cde0},
issn = {1939-1382},
journal = {IEEE Transactions on Learning Technologies},
number = 1,
publisher = {IEEE Computer Society},
title = {An Approach to Folksonomy-based Ontology Maintenance for Learning Environments},
url = {http://www.computer.org/portal/web/csdl/doi/10.1109/TLT.2011.21},
volume = 99,
year = 2011
}
@inproceedings{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},
groups = {public},
interhash = {f733d993459329ed1ef9f26d303ba0d9},
intrahash = {efc1396e845f3db1688dc8ef154d9520},
lastdatemodified = {2007-01-04},
lastname = {Christiaens},
own = {notown},
pdf = {christiaens06-metadata.pdf},
publisher = {Springer},
read = {notread},
timestamp = {2007-09-11 13:31:23},
title = {Metadata Mechanisms: From Ontology to Folksonomy ... and Back},
url = {http://www.springerlink.com/content/m370107220473394},
username = {dbenz},
workshoppub = {1},
year = 2006
}
@article{Hazman:30May2009:1744-2621:24,
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.},
author = {Hazman, Maryam and El-Beltagy, Samhaa R. and Rafea, Ahmed},
doi = {doi:10.1504/IJMSO.2009.026251},
interhash = {fe27d687bcba91a7a6fe51eec9a2b87d},
intrahash = {323c8bdedc8a4643232a498ac03d6407},
journal = {International Journal of Metadata, Semantics and Ontologies},
pages = {24-33(10)},
title = {Ontology learning from domain specific web documents},
url = {http://www.ingentaconnect.com/content/ind/ijmso/2009/00000004/F0020001/art00003},
volume = 4,
year = 2009
}
@inproceedings{maedche2002measuring,
address = {London, UK},
author = {Maedche, Alexander and Staab, Steffen},
booktitle = {EKAW '02: Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web},
file = {:maedche2002measuring.pdf:PDF},
interhash = {d5b06cd1af41e25a751ab755fb3a0068},
intrahash = {3a3b029259f39e1e1893012f5e8a7b1e},
pages = {251--263},
publisher = {Springer-Verlag},
title = {Measuring Similarity between Ontologies},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.131.5761&rep=rep1&type=pdf},
year = 2002
}
@inproceedings{mchale1998comparison,
abstract = { This paper presents the results of using Roget's International Thesaurus as the taxonomy in a semantic similarity measurement task. Four similarity metrics were taken from the literature and applied to Roget's The experimental evaluation suggests that the traditional edge counting approach does surprisingly well (a correlation of r=0.88 with a benchmark set of human similarity judgements, with an upper bound of r=0.90 for human subjects performing the same task.)},
author = {McHale, Michael},
booktitle = {Proceedings of the COLING/ACL Workshop on Usage of WordNet in Natural Language Processing Systems, August 16, 1998, Montreal, Canada},
editor = {Harabagiu, Sanda and Chai, Joyce Yue},
interhash = {3759256d578dcab653d72a42dc4a3f0e},
intrahash = {070f5400952a1847392ce7cd4522eacd},
publisher = {Association for Computational Linguistics, Morristown, NJ, USA},
title = {A Comparison of WordNet and Roget's Taxonomy for Measuring Semantic Similarity},
url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9809003},
year = 1998
}
@inproceedings{jarmasz2003rogets,
abstract = {We have implemented a system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles� list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough�s 65 pairs, which we have also studied. Our Roget�s-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget�s and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader�s Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75\%, 82.00\% and 74.33\% of the questions respectively.},
author = {Jarmasz, Mario and Szpakowicz, Stan},
booktitle = {Conference on Recent Advances in Natural Language Processing},
interhash = {e28cc3a4231e064f44cfdb2e3338aaf3},
intrahash = {acde39a427ef0e7501f07e8b067a88f0},
pages = {212--219},
title = {Roget's thesaurus and semantic similarity},
url = {http://www.site.uottawa.ca/~mjarmasz/pubs/jarmasz_roget_sim.pdf},
year = 2003
}
@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
}
@techreport{benz2004user,
address = {Freiburg, Germany},
author = {Benz, Dominik},
bla = {blub},
institution = {University of Freiburg},
interhash = {aeb8ad23a09ff41198efe472b95015d2},
intrahash = {2841725013ad4f7026fcc1f6920e3da8},
month = {August},
pi = {pa},
title = {User models and Ontologies},
year = 2004
}
@article{macgregor2006collaborative,
abstract = {Purpose � The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence. The paper reviews the related literature and discusses some of the problems associated with, and the potential of, collaborative tagging approaches for knowledge organisation and general resource discovery. Design/methodology/approach � A definition of controlled vocabularies is proposed and used to assess the efficacy of collaborative tagging. An exposition of the collaborative tagging model is provided and a review of the major contributions to the tagging literature is presented. Findings � There are numerous difficulties with collaborative tagging systems (e.g. low precision, lack of collocation, etc.) that originate from the absence of properties that characterise controlled vocabularies. However, such systems can not be dismissed. Librarians and information professionals have lessons to learn from the interactive and social aspects exemplified by collaborative tagging systems, as well as their success in engaging users with information management. The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal. Research limitations/implications � Librarians and information professional researchers should be taking a lead role in research aimed at assessing the efficacy of collaborative tagging in relation to information storage, organisation, and retrieval, and to influence the future development of collaborative tagging systems. Practical implications � The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information. Originality/value � At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate.},
author = {Macgregor, George and Mcculloch, Emma},
file = {macgregor2006collaborative.pdf:macgregor2006collaborative.pdf:PDF},
interhash = {8d7a458fb6f9ff722c7d02104ec6dbd0},
intrahash = {583976b7d64ff0b140827342e73e70d2},
journal = {Library Review},
lastdatemodified = {2006-07-17},
lastname = {Macgregor},
number = 5,
own = {own},
pdf = {macgregor06-collaborative.pdf},
read = {readnext},
title = {Collaborative Tagging as a Knowledge Organisation and Resource Discovery Tool},
url = {eprints.rclis.org/archive/00005703/},
volume = 55,
year = 2006
}
@book{staab2004handbook,
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {Handbook on Ontologies},
editor = {Staab, Steffen and Studer, Rudi},
interhash = {494a7427b9dd11496d824c824b35938b},
intrahash = {28269590c9d9c1660c1d7c98a73a28e1},
isbn = {3-540-40834-7},
publisher = {Springer},
series = {International Handbooks on Information Systems},
title = {Handbook on Ontologies},
year = 2004
}
@misc{gruber2005ontology,
abstract = {Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by formal terms used in data that they might generate or consume. Folksonomies are an emergent phenomenon of the social web. They are created as people associate terms with content that they generate or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This piece is an attempt to shed some cool light on the subject, and to preview some new work that applies the two ideas together to enable an Internet ecology for folksonomies.},
author = {Gruber, Tom},
file = {gruber2005ontology.pdf:gruber2005ontology.pdf:PDF},
interhash = {95dcd92534079ba054d4301522ac45f9},
intrahash = {3179f257b1d843da3ae1de136eec8318},
lastdatemodified = {2006-07-19},
lastname = {Gruber},
longnotes = {[[http://tomgruber.org/writing/tagontology-tagcamp-talk.pdf slides(pdf)]]},
own = {own},
pdf = {gruber05-ontology.pdf},
read = {readnext},
title = {Ontology of Folksonomy},
url = {tomgruber.org/writing/ontology-of-folksonomy.htm},
year = 2005
}
@inproceedings{razmerita2003ontology,
author = {Razmerita, L. and Angehrn, A. and Maedche, A.},
booktitle = {Proceedings of the User Modeling Conference},
interhash = {22705bc1964644b4ae40ec26306eb93e},
intrahash = {38f46f38f7dd402bbc4192bfe8ce8016},
location = {Pittsburgh, USA},
pages = {213--217},
publisher = {Springer-Verlag},
title = {Ontology based user modeling for Knowledge Management Systems},
year = 2003
}
@inproceedings{razmerita2003role,
author = {Razmerita, L. and Angehrn, A. and Nabeth, T.},
booktitle = {Proceedings of HCI International},
interhash = {ecf4fe64704bf2ccad8b0379b1bedbe6},
intrahash = {3837f07bf684641c483b900ec6e25521},
location = {Greece},
pages = {450--456},
title = {On the role of user models and user modeling in Knowledge Management Systems},
volume = 2,
year = 2003
}
@article{middleton2004ontological,
author = {Middleton, Stuart E. and Shadbolt, Nigel R. and {De Roure}, David C.},
interhash = {c0bcba5b8f31cfbe434062d77057904e},
intrahash = {a3516024369f530bcf3fb37d89aea498},
issn = {1046-8188},
journal = {ACM Trans. Inf. Syst.},
number = 1,
pages = {54--88},
publisher = {ACM Press},
title = {Ontological user profiling in recommender systems},
url = {doi.acm.org/10.1145/963770.963773},
volume = 22,
year = 2004
}
@inproceedings{middleton2003capturing,
author = {Middleton, Stuart E. and Shadbolt, Nigel R. and {De Roure}, David C.},
booktitle = {Proceedings of the international conference on Knowledge capture},
interhash = {dbba6859beb8d94bebc21a74140e746d},
intrahash = {09268a88aba1913bd7901cfb40819f71},
isbn = {1-58113-583-1},
location = {Sanibel Island, FL, USA},
pages = {62--69},
publisher = {ACM Press},
title = {Capturing interest through inference and visualization: ontological user profiling in recommender systems},
url = {doi.acm.org/10.1145/945645.945657},
year = 2003
}
@inproceedings{middleton2002exploiting,
author = {Middleton, Stuart E. and Alani, H. and Shadbolt, Nigel R. and {De Roure}, David C.},
booktitle = {Proceedings of the 11th International World Wide Web Conference WWW-2002},
interhash = {4aea05259e0bdc2b9001b7ce11c10ac0},
intrahash = {4e6b8b4a669587d142338c5ccd1be4bb},
location = {Hawaii, USA},
title = {Exploiting Synergy Between Ontologies and Recommender System},
year = 2002
}
@inproceedings{middleton2001capturing,
author = {Middleton, Stuart E. and {De Roure}, David C. and Shadbolt, Nigel R.},
booktitle = {Proceedings of the international conference on Knowledge capture},
interhash = {912fba5e828e72d665ba40e7607f1d97},
intrahash = {0d38005e912a8511eff1809776ed292f},
isbn = {1-58113-380-4},
location = {Victoria, British Columbia, Canada},
pages = {100--107},
publisher = {ACM Press},
title = {Capturing knowledge of user preferences: ontologies in recommender systems},
url = {doi.acm.org/10.1145/500737.500755},
year = 2001
}
@techreport{kobsa2001adaptive,
author = {Kobsa, Alfred},
institution = {University of California, Irvine},
interhash = {9b37d4d487c7253e9b417bd9d0cad161},
intrahash = {c0e571f59148ba509446b11e11c413d3},
lastdatemodified = {2005-08-08},
lastname = {Kobsa},
own = {notown},
read = {notread},
title = {Adaptive Verfahren - Benutzermodellierung},
year = 2001
}
@book{hastie2003elements,
author = {Hastie, T. and Tibshirani, R. and Friedman, J.},
interhash = {81859d2f50e11fd01fb7fe0f89d030ea},
intrahash = {303c4013b673eee0be5a9b1ba3b04e3b},
note = {chapter 10},
publisher = {Springer},
title = {The Elements of Statistical learning},
year = 2003
}
@inproceedings{browne1993experieces,
address = {Amsterdam, Netherlands, Elsevier},
author = {Browne, D.},
booktitle = {M.Schneider-Hufschmidt, T.K�hme and U. Malinowski, eds: Adaptive User Interfaces: Principles and Practise},
interhash = {acacc5fe06678c664374c3618b7598ee},
intrahash = {6dd5dbad6a25fc27df6495992439b15b},
title = {Experieces from the AID Project},
year = 1993
}
@techreport{benz2004description,
address = {Germany},
author = {Benz, Dominik},
institution = {Institute of Computer Science, University of Freiburg},
interhash = {b28df619746b25277c6e64645f6f622d},
intrahash = {2aa54b86f52530fb24eed7b8b776eaa9},
lastdatemodified = {2007-05-25},
lastname = {Benz},
month = {February},
note = {Elaboration in the context of the Seminar "Semantic Web" by Prof. Lausen, winter term 03/04},
own = {notown},
read = {notread},
title = {Description Logics - the logical foundation of the Semantic Web},
year = 2004
}
@inproceedings{schmitz2006inducing,
address = {Edinburgh, Scotland},
author = {Schmitz, Patrick},
booktitle = {Collaborative Web Tagging Workshop at WWW 2006},
file = {schmitz2006inducing.pdf:schmitz2006inducing.pdf:PDF},
interhash = {1335f4ef87f951e6edf4fd94f885d3a2},
intrahash = {5a9065e96237a69d95edebc03ccac92d},
month = may,
pdf = {schmitz2006inducing.pdf},
title = {Inducing Ontology from Flickr Tags.},
year = 2006
}
@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
}