@article{gemmell2008personalizing,
  abstract = {The popularity of collaborative tagging, otherwise known as “folksonomies�?, emanate from the flexibility they afford usersin navigating large information spaces for resources, tags, or other users, unencumbered by a pre-defined navigational orconceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: usersare free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous,or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregateuser models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervisedhierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clustersprovide the nexus between the user and those resources that correspond more closely to the user’s intent. We validate thisassertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site.},
  author = {Gemmell, Jonathan and Shepitsen, Andriy and Mobasher, Bamshad and Burke, Robin},
  file = {gemmell2008personalizing.pdf:gemmell2008personalizing.pdf:PDF},
  groups = {public},
  interhash = {e544ba095f411429896b11fd3f94fd5c},
  intrahash = {2e0535788c372e98e49646873cea4e1e},
  journal = {Data Warehousing and Knowledge Discovery},
  journalpub = {1},
  pages = {196--205},
  timestamp = {2009-08-10 10:30:08},
  title = {Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering},
  url = {http://dx.doi.org/10.1007/978-3-540-85836-2_19},
  username = {dbenz},
  year = 2008
}

@conference{gabrilovich2007computing,
  author = {Gabrilovich, E. and Markovitch, S.},
  booktitle = {Proceedings of the 20th International Joint Conference on Artificial Intelligence},
  file = {gabrilovich2007computing.pdf:gabrilovich2007computing.pdf:PDF},
  interhash = {5baf6af4bf58cf3926b39a12edb35e58},
  intrahash = {839a06f838f02c04a8569fd41a5da284},
  pages = {6--12},
  title = {{Computing semantic relatedness using wikipedia-based explicit semantic analysis}},
  url = {http://scholar.google.de/scholar.bib?q=info:woCrRNTAsA4J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=3},
  year = 2007
}

@inproceedings{hjelm2008multilingual,
  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.},
  author = {Hjelm, Hans and Buitelaar, Paul},
  booktitle = {ECAI},
  crossref = {conf/ecai/2008},
  editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.},
  ee = {http://dx.doi.org/10.3233/978-1-58603-891-5-288},
  file = {hjelm2008multilingual.pdf:hjelm2008multilingual.pdf:PDF},
  groups = {public},
  interhash = {21a658154fb1a02e773b7a678b15f9f4},
  intrahash = {813903a333a40ecf9a59ded552acb323},
  isbn = {978-1-58603-891-5},
  pages = {288-292},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  timestamp = {2011-01-18 12:06:01},
  title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.},
  url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf},
  username = {dbenz},
  volume = 178,
  year = 2008
}

@inproceedings{lalwani2009deriving,
  abstract = {In this paper we describe our investigation of tagging systems and the derivation of ontological structure in the form of a folksonomy from the set of tags. Tagging systems are becoming popular, because the amount of information available on some websites is becoming too large for humans to browse manually and the types of information (multimedia data) is unsuitable for the indexers used by conventional search engines to organize. However, tag-based search is very inaccurate and incomplete (low precision and recall), because the semantics of the tags is both weak and ambiguous. The basic problem is that tags are treated like keywords by search engines, which consider individual tags in isolation. However, there is additional semantics implicit in a collection of tagged data. In this paper, we innovate and investigate techniques to make the implicit semantics explicit, so that search can be improved in both precision and recall and additional utility can be derived from the tags that people associate with multimedia items (pictures, blogs, videos, etc.). Our approach is to propose hypotheses about the ontological structure inherent in a collection of tags and then attempt to verify the hypotheses statistically. We conducted more than one hundred experimental searches on Flickr with different tags and discovered by statistical analysis information about how tags are assigned by users and what ontological knowledge is implicit in these tags that can be made explicit, and ultimately, exploited.},
  address = {New York, NY, USA},
  author = {Lalwani, Saurabh and Huhns, Michael N.},
  booktitle = {ACM-SE 47: Proceedings of the 47th Annual Southeast Regional Conference},
  doi = {http://doi.acm.org/10.1145/1566445.1566512},
  file = {lalwani2009deriving.pdf:lalwani2009deriving.pdf:PDF},
  groups = {public},
  interhash = {84ca9a71836a3475f601f6522e1d4da1},
  intrahash = {5d2c6d723aa835c4f245723200f93173},
  isbn = {978-1-60558-421-8},
  location = {Clemson, South Carolina},
  pages = {1--2},
  publisher = {ACM},
  timestamp = {2010-01-18 20:11:04},
  title = {Deriving ontological structure from a folksonomy},
  url = {http://portal.acm.org/citation.cfm?id=1566445.1566512},
  username = {dbenz},
  year = 2009
}

@inproceedings{liu2008between,
  abstract = {We present our first user study of CRAFT, a semantic prototype for collaborative investigation and analysis, which allows users to extend the system's ontology to capture new concepts as they conduct their work. We devised a paradigm in which multiple series of ontologies evolve in different trajectories from the same initial point. We analyze the ontology evolution quantitatively with several metrics, and user behavior qualitatively through interviews and observation. Based on our study, we propose a set of design suggestions for semantic applications with collaborative and implicit ontology development.},
  address = {New York, NY, USA},
  author = {Liu, Jiahui and Gruen, Daniel M.},
  booktitle = {IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/1378773.1378830},
  file = {liu2008between.pdf:liu2008between.pdf:PDF},
  groups = {public},
  interhash = {45e8362f274501ec6b2a81e2693b405e},
  intrahash = {8c92fc47200127eeb84f2964a3d1b528},
  isbn = {978-1-59593-987-6},
  location = {Gran Canaria, Spain},
  pages = {361--364},
  publisher = {ACM},
  timestamp = {2010-01-18 20:13:25},
  title = {Between ontology and folksonomy: a study of collaborative and implicit ontology evolution},
  url = {http://portal.acm.org/citation.cfm?id=1378830},
  username = {dbenz},
  year = 2008
}

@inproceedings{mori2006extracting,
  abstract = {Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.},
  author = {Mori, Junichiro and Tsujishita, Takumi and Matsuo, Yutaka and Ishizuka, Mitsuru},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2006},
  ee = {http://dx.doi.org/10.1007/11926078_35},
  file = {mori2006extracting.pdf:mori2006extracting.pdf:PDF},
  groups = {public},
  interhash = {457973d894180bd95e99bb6f7bb5cbc5},
  intrahash = {f1a145a60c3e4d39e91b39a7c1178110},
  pages = {487-500},
  timestamp = {2009-06-01 15:32:20},
  title = {Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts},
  username = {dbenz},
  year = 2006
}

@inproceedings{ramage2009clustering,
  abstract = {Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase.},
  address = {New York, NY, USA},
  author = {Ramage, Daniel and Heymann, Paul and Manning, Christopher D. and Garcia-Molina, Hector},
  booktitle = {WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining},
  doi = {http://doi.acm.org/10.1145/1498759.1498809},
  file = {ramage2009clustering.pdf:ramage2009clustering.pdf:PDF},
  groups = {public},
  interhash = {5595f06f88310ed67fd6fe23f813c69b},
  intrahash = {75c4bad29d7eb4b34f68da27f0353516},
  isbn = {978-1-60558-390-7},
  location = {Barcelona, Spain},
  pages = {54--63},
  publisher = {ACM},
  timestamp = {2009-04-24 10:19:45},
  title = {Clustering the tagged web},
  url = {http://portal.acm.org/citation.cfm?id=1498809},
  username = {dbenz},
  year = 2009
}

@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
}

@inbook{specia2007integrating,
  abstract = {While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.},
  author = {Specia, Lucia and Motta, Enrico},
  file = {specia2007integrating.pdf:specia2007integrating.pdf:PDF},
  interhash = {b828fbd5c9ddc4f9551f973445ecb283},
  intrahash = {8800fc1a639aeb43fd55598d2410e2e1},
  pages = {624-639},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Integrating Folksonomies with the Semantic Web},
  volume = {4519/2007},
  year = 2007
}

@inproceedings{wagner2010wisdom,
  abstract = {Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called \tweetonomy", together with a set of stream-based measures that allow researchers to systematically define and compare different stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical findings demonstrate that different social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.},
  author = {Wagner, C. and Strohmaier, M.},
  booktitle = {Proc. of the Semantic Search 2010 Workshop (SemSearch2010)},
  file = {wagner2010wisdom.pdf:wagner2010wisdom.pdf:PDF},
  groups = {public},
  interhash = {02c222a4f9abd5964ea61af034769af4},
  intrahash = {2f96232a648d4fd1617c389d899f3d2b},
  location = {Raleigh, NC, USA},
  month = {april},
  timestamp = {2010-04-19 08:03:47},
  title = {The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams},
  url = {http://mstrohm.wordpress.com/2010/04/17/on-taxonomies-folksonomies-and-tweetonomies/},
  username = {dbenz},
  year = 2010
}

@article{wang2009probabilistic,
  abstract = {Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. We propose two algorithms for learning terminological ontologies using the principle of topic relationship and exploiting information theory with the probabilistic topic models learned. Experiments with different model parameters were conducted and learned ontology statements were evaluated by the domain experts. We have also compared the results of our method with two existing concept hierarchy learning methods on the same dataset. The study shows that our method outperforms other methods in terms of recall and precision measures. The precision level of the learned ontology is sufficient for it to be deployed for the purpose of browsing, navigation, and information search and retrieval in digital libraries.},
  address = {Los Alamitos, CA, USA},
  author = {Wang, Wei and Barnaghi, Payam and Bargiela, Andrzej},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.122},
  file = {wang2009probabilistic.pdf:wang2009probabilistic.pdf:PDF},
  groups = {public},
  interhash = {c8f8ce2267199bd80eeda718833ba55e},
  intrahash = {478d8613c72a970a99837fa59989cfff},
  issn = {1041-4347},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  journalpub = {1},
  number = {RapidPosts},
  publisher = {IEEE Computer Society},
  timestamp = {2010-05-27 15:29:10},
  title = {Probabilistic Topic Models for Learning Terminological Ontologies},
  username = {dbenz},
  volume = 99,
  year = 2009
}

@inproceedings{baezayates2007extracting,
  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.},
  address = {New York, NY, USA},
  author = {Baeza-Yates, Ricardo and Tiberi, Alessandro},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281204},
  file = {baezayates2007extracting.pdf:baezayates2007extracting.pdf:PDF},
  groups = {public},
  interhash = {26ca034be705abaf072835784f53d877},
  intrahash = {6e45b65feffd1545c6dca62bf4b8f53d},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {76--85},
  publisher = {ACM},
  timestamp = {2009-06-01 15:31:03},
  title = {Extracting semantic relations from query logs},
  url = {http://portal.acm.org/citation.cfm?id=1281192.1281204},
  username = {dbenz},
  year = 2007
}

@incollection{garciasilva2008pattern,
  abstract = {With the goal of speeding up the ontology development process, ontology engineers are starting to reuse as much as possible available ontologies and non-ontological resources such as classification schemes, thesauri, lexicons and folksonomies, that already have some degree of consensus. The reuse of such non-ontological resources necessarily involves their re-engineering into ontologies. Non-ontological resources are highly heterogeneous in their data model and contents: they encode different types of knowledge, and they can be modeled and implemented in different ways. In this paper we present (1) a typology for non-ontological resources, (2) a pattern based approach for re-engineering non-ontological resources into ontologies, and (3) a use case of the proposed approach.},
  at = {2009-02-12 17:08:10},
  author = {Garc\'{i}a-Silva, Andr\'{e}s and G\'{o}mez-P\'{e}rez, Asunci\'{o}n and Su\'{a}rez-Figueroa, Mari and Villaz\'{o}n-Terrazas, Boris},
  doi = {http://dx.doi.org/10.1007/978-3-540-89704-0\_12},
  file = {garciasilva2008pattern.pdf:garciasilva2008pattern.pdf:PDF},
  groups = {public},
  interhash = {f09d71443dff615a314c435df89a3d39},
  intrahash = {98096132b1eb4b4b5b0de3cec6a22de5},
  journal = {The Semantic Web},
  misc_id = {4039576},
  pages = {167--181},
  priority = {2},
  timestamp = {2009-09-24 23:29:10},
  title = {A Pattern Based Approach for Re-engineering Non-Ontological Resources into Ontologies},
  url = {http://dx.doi.org/10.1007/978-3-540-89704-0\_12},
  username = {dbenz},
  year = 2008
}

@article{girju2006automatic,
  abstract = {An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part–whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part–whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part–whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part–whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.},
  author = {Girju, Roxana and Badulescu, Adriana and Moldovan, Dan I.},
  ee = {http://dx.doi.org/10.1162/coli.2006.32.1.83},
  file = {girju2006automatic.pdf:girju2006automatic.pdf:PDF},
  groups = {public},
  interhash = {e3b517e5895171e35375ce08d632d738},
  intrahash = {ce346613f91431251a6fe867f4360378},
  journal = {Computational Linguistics},
  journalpub = {1},
  number = 1,
  pages = {83-135},
  timestamp = {2010-10-25 15:08:53},
  title = {Automatic Discovery of Part-Whole Relations.},
  url = {http://dblp.uni-trier.de/db/journals/coling/coling32.html#GirjuBM06},
  username = {dbenz},
  volume = 32,
  year = 2006
}

@inproceedings{snow2006semantic,
  abstract = {We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10; 000 novel synsets to WordNet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.},
  author = {Snow, Rion and Jurafsky, Daniel and Ng, Andrew Y.},
  booktitle = {ACL},
  crossref = {conf/acl/2006},
  ee = {http://acl.ldc.upenn.edu/P/P06/P06-1101.pdf},
  file = {snow2006semantic.pdf:snow2006semantic.pdf:PDF},
  groups = {public},
  interhash = {c0f5a3a22faa8dc4b61c9a717a6c9037},
  intrahash = {8f39e7ac43a97719c5a746da02dbd964},
  publisher = {The Association for Computer Linguistics},
  timestamp = {2010-10-25 15:06:10},
  title = {Semantic Taxonomy Induction from Heterogenous Evidence.},
  url = {http://dblp.uni-trier.de/db/conf/acl/acl2006.html#SnowJN06},
  username = {dbenz},
  year = 2006
}

@article{zhou2007ontology,
  0 = {http://dx.doi.org/10.1007/s10799-007-0019-5},
  abstract = {Abstract\ \ Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.},
  at = {2009-02-13 15:22:56},
  author = {Zhou, Lina},
  doi = {10.1007/s10799-007-0019-5},
  file = {zhou2007ontology.pdf:zhou2007ontology.pdf:PDF},
  groups = {public},
  interhash = {78b6d3db998dcd27c475dfff3816f48f},
  intrahash = {95b0f4f7c9c628e032d8bb4c69b432ed},
  journal = {Information Technology and Management},
  journalpub = {1},
  misc_id = {1719627},
  number = 3,
  pages = {241--252},
  priority = {3},
  timestamp = {2010-06-01 16:18:37},
  title = {Ontology learning: state of the art and open issues},
  url = {http://www.springerlink.com/content/j4g22112l7k00833/},
  username = {dbenz},
  volume = 8,
  year = 2007
}

@article{zhou2008hierarchical,
  abstract = {This paper proposes a novel tree kernel-based method with rich syntactic and semantic information for the extraction of semantic relations between named entities. With a parse tree and an entity pair, we first construct a rich semantic relation tree structure to integrate both syntactic and semantic information. And then we propose a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering the paths of their ancestor nodes as their contexts to capture structural information in the tree structure. An evaluation on the Automatic Content Extraction/Relation Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernelbased method outperforms other state-of-the-art methods.},
  address = {Tarrytown, NY, USA},
  author = {Zhou, GuoDong and Zhang, Min and Ji, DongHong and Zhu, QiaoMing},
  doi = {http://dx.doi.org/10.1016/j.ipm.2007.07.007},
  file = {zhou2008hierarchical.pdf:zhou2008hierarchical.pdf:PDF},
  groups = {public},
  interhash = {e5e2d51cf1f3a6d5efc3bd25c40602c8},
  intrahash = {b7eb173bc2c3dd1311a24ae9a96e5c2c},
  issn = {0306-4573},
  journal = {Information Process Managegement},
  journalpub = {1},
  number = 3,
  pages = {1008--1021},
  publisher = {Pergamon Press, Inc.},
  timestamp = {2010-06-10 10:51:05},
  title = {Hierarchical learning strategy in semantic relation extraction},
  url = {http://nlp.suda.edu.cn/~gdzhou/publication/zhougd2010_INS_ContextSensitiveTreeKernelforRelationExtraction.pdf},
  username = {dbenz},
  volume = 44,
  year = 2008
}

@inproceedings{tesconi2008semantify,
  author = {Tesconi, Maurizio and Ronzano, Francesco and Marchetti, Andrea and Minutoli, Salvatore},
  crossref = {CEUR-WS.org/Vol-405},
  file = {tesconi2008semantify.pdf:tesconi2008semantify.pdf:PDF},
  interhash = {0c1c96b41a0af8512c20a7d41504640f},
  intrahash = {348a962fe13e0b605ffc53d592464c24},
  title = {Semantify del.icio.us: Automatically Turn your Tags into Senses},
  url = {http://CEUR-WS.org/Vol-405/paper8.pdf},
  year = 2008
}

@inproceedings{palla07-centrality,
  author = {Pollner, Peter and Palla, Gergely and Abel, Daniel and Vicsek, Andras and Farkas, Illes J. and Derenyi, Imre and Vicsek, Tamas},
  booktitle = {Proceedings of the European Conference of Complex Systems (ECCS'07)},
  interhash = {f72290a2eb48fee7ed33f7ea12a08acc},
  intrahash = {1963dda8661c18628be7c52abec93111},
  title = {Centrality properties of directed module members in social networks},
  year = 2007
}