@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
}
@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
}
@article{eda2009effectiveness,
abstract = {In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like “cool” and “fun” from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.},
author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
doi = {10.1007/s11280-009-0069-1},
interhash = {a560796c977bc7582017f662bf88c16d},
intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
issn = {1386-145X},
journal = {World Wide Web},
number = 4,
pages = {421--440},
publisher = {Springer Netherlands},
title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
url = {http://dx.doi.org/10.1007/s11280-009-0069-1},
volume = 12,
year = 2009
}
@inproceedings{kim2008state,
abstract = {There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.},
author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee},
booktitle = {DCMI '08: Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications},
interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc},
intrahash = {cb56167e7e5e0dbfee017671064ff81e},
location = {Berlin, Germany},
pages = {128--137},
publisher = {Dublin Core Metadata Initiative},
title = {The state of the art in tag ontologies: a semantic model for tagging and folksonomies},
url = {http://portal.acm.org/citation.cfm?id=1503418.1503431},
year = 2008
}
@article{lux2008folksonomies,
abstract = {Is Web 2.0 just hype or just a buzzword, which might disappear in the near future One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 the folksonomy and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.},
author = {Lux, Mathias and Dösinger, Gisela},
doi = {10.1504/IJKL.2007.016709},
interhash = {5dde7a91231320f96c0c4b3e7ba9a503},
intrahash = {dd5cdcc6449d97622033bbebcd4d1874},
journal = {International Journal of Knowledge and Learning},
month = jan,
number = {4-5},
pages = {515--528},
title = {From folksonomies to ontologies: employing wisdom of the crowds to serve learning purposes},
url = {http://www.ingentaconnect.com/content/ind/ijkl/2008/00000003/F0020004/art00009},
volume = 3,
year = 2008
}
@inproceedings{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.},
address = {Berlin/Heidelberg},
author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York},
booktitle = {The Semantic Web: Research and Applications},
doi = {10.1007/11431053_33},
editor = {Gómez-Pérez, Asuncion and Euzenat, Jerome},
interhash = {c9ba81293a1b27f1c9bdf38a3beec060},
intrahash = {258348df63fd814cb7e4ccc9762f9d8c},
isbn = {3-540-26124-9},
pages = {486--499},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.},
url = {http://dx.doi.org/10.1007/11431053_33},
volume = 3532,
year = 2005
}
@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},
interhash = {8b02d2b3fdbb97c3db6e3b23079a56e5},
intrahash = {bf6f73d2ef74ca6f1d355fb5688b673c},
isbn = {978-1-59593-597-7},
pages = {103--110},
publisher = {ACM Press},
title = {Towards automatic extraction of event and place semantics from flickr tags},
url = {http://dx.doi.org/10.1145/1277741.1277762},
year = 2007
}
@inproceedings{kim2007tag,
abstract = {In this paper we give an overview of the int.ere.st for a social tagging, bookmarking, and sharing service. It is based on the SCOT ontology. The SCOT ontology can represent the structure and semantics for social tagging data and provide methods for sharing and reusing them. We describe how it enables users to participate in a semantic social tagging from functional point of view and show how int.ere.st allows users to save, tag, and search SCOT ontologies. All kinds of user contributions in the system will be exposed as RDF vocabularies that connect
them. We believe it is a good starting point to build Semantic Web based society using tagging data.
},
author = {Kim, Hak Lae and Yang, Sung-Kwon and Song, Seung-Jae and Breslin, John G. and Kim, Hong-Gee},
booktitle = {Proceedings of the Semantic Web Challenge 2007},
editor = {Golbeck, Jennifer and Mika, Peter},
interhash = {2067db51319e25598ae6c029fc691039},
intrahash = {70220de9a66ea2818bc16a7fa5e2c7ae},
issn = {1613-0073},
series = {CEUR-WS.org},
title = {Tag Mediated Society with {SCOT} Ontology},
url = {http://ceur-ws.org/Vol-295/paper14.pdf},
volume = 295,
year = 2007
}
@inproceedings{cattuto2008semantic,
abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.},
address = {Patras, Greece},
author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)},
interhash = {cc62b733f6e0402db966d6dbf1b7711f},
intrahash = {3b0aca61b24e4343bd80390614e3066e},
isbn = {978-960-89282-6-8},
month = jul,
pages = {39--43},
title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems},
url = {http://olp.dfki.de/olp3/},
year = 2008
}
@inproceedings{benz07ontology,
abstract = {The emergence of collaborative tagging systems with their underlying flat and
uncontrolled resource organization paradigm has led to a large number of
research activities focussing on a formal description and analysis of the
resulting "folksonomies". An interesting outcome is that the characteristic
qualities of these systems seem to be inverse to more traditional knowledge
structuring approaches like taxonomies or ontologies: The latter provide rich
and precise semantics, but suffer - amongst others - from a knowledge
acquisition bottleneck. An important step towards exploiting the possible
synergies by bridging the gap between both paradigms is the automatic
extraction of relations between tags in a folksonomy. This position paper
presents preliminary results of ongoing work to induce hierarchical
relationships among tags by analyzing the aggregated data of collaborative
tagging systems as a basis for an ontology learning procedure.
},
address = {Halle/Saale},
author = {Benz, Dominik and Hotho, Andreas},
booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)},
editor = {Hinneburg, Alexander},
interhash = {ff7de5717f771dabd764675279ff3adf},
intrahash = {72bff5ebe5dfb5023f62ba9b94e6ed01},
isbn = {978-3-86010-907-6},
month = sep,
pages = {109--112},
publisher = {Martin-Luther-Universität Halle-Wittenberg},
title = {Position Paper: Ontology Learning from Folksonomies},
url = {http://lwa07.informatik.uni-halle.de/kdml07/kdml07.htm},
year = 2007
}
@misc{darcus2008bibliographic,
abstract = {The Bibliographic Ontology Specification provides main concepts and properties for describing citations and bibliographic references (i.e. quotes, books, articles, etc) on the Semantic Web.},
author = {D'Arcus, Bruce and Giasson, Frédérick},
editor = {Giasson, Frédérick},
howpublished = {Specification Document},
interhash = {9a7903afc37b62c3bbeaebbf8023c5db},
intrahash = {209d061c6809463a86308a8091eace8f},
title = {Bibliographic Ontology Specification},
url = {http://bibliontology.com/},
year = 2008
}
@article{voelker2008aeon,
abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.},
address = {Amsterdam, The Netherlands, The Netherlands},
author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas},
interhash = {f14794f4961d0127dc50c1938eaef7ea},
intrahash = {f8f0bb3e3495e7627770b470d1a5f1a3},
issn = {1570-5838},
journal = {Applied Ontology},
number = {1-2},
pages = {41--62},
publisher = {IOS Press},
title = {AEON - An approach to the automatic evaluation of ontologies},
url = {http://portal.acm.org/citation.cfm?id=1412422},
volume = 3,
year = 2008
}
@inproceedings{middleton02,
abstract = {Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.},
author = {Middleton, Stuart E. and Alani, Harith and Roure, David C. De},
booktitle = {Proceedings of the WWW2002 International Workshop on the Semantic Web},
interhash = {a098783b2b8f386218c3312ebcfa6286},
intrahash = {401e667028f6a4674bb5403ec680d7f3},
note = {cite arxiv:cs.LG/0204012
Comment: Semantic web conference, WWW2002, 10 pages},
title = {Exploiting Synergy Between Ontologies and Recommender Systems},
url = {http://arxiv.org/abs/cs/0204012},
year = 2002
}
@inproceedings{middleton01,
abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.},
address = {New York, NY, USA},
author = {Middleton, Stuart E. and Roure, David C. De and Shadbolt, Nigel R.},
booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture},
doi = {http://doi.acm.org/10.1145/500737.500755},
interhash = {332dfc15a8f0fc442b47a9a4b740b1bf},
intrahash = {6d0a7792db2c0f96bd0a495a56e57464},
isbn = {1-58113-380-4},
location = {Victoria, British Columbia, Canada},
pages = {100--107},
publisher = {ACM},
title = {Capturing knowledge of user preferences: ontologies in recommender systems},
url = {http://portal.acm.org/citation.cfm?id=500737.500755},
year = 2001
}
@inproceedings{schmitz2006inducing,
address = {Edinburgh, Scotland},
author = {Schmitz, Patrick},
booktitle = {Collaborative Web Tagging Workshop at WWW 2006},
interhash = {1335f4ef87f951e6edf4fd94f885d3a2},
intrahash = {5a9065e96237a69d95edebc03ccac92d},
month = May,
title = {Inducing Ontology from Flickr Tags.},
year = 2006
}