http://www.bibsonomy.org/burst/tag/relationBibSonomy BuRST Feed for /tag/relation2008-07-27T03:55:24+02:00http://www.bibsonomy.org/bibtex/2f1a145a60c3e4d39e91b39a7c1178110/beatebeate2008-07-17T00:11:22+02:00ie semantic-web relation social-networks <span style="color:#555555;">Junichiro <a href="http://www.bibsonomy.org/author/Mori">Mori</a> and Takumi <a href="http://www.bibsonomy.org/author/Tsujishita">Tsujishita</a> and Yutaka <a href="http://www.bibsonomy.org/author/Matsuo">Matsuo</a> and Mitsuru <a href="http://www.bibsonomy.org/author/Ishizuka">Ishizuka</a> </span><em>International Semantic Web Conference, </em><em>page487-500. </em>(<em>2006</em>)Thu Jul 17 00:11:22 CEST 2008International Semantic Web ConferenceDBLP:conf/semweb/2006487-500Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts2006ie semantic-web relation social-networks http://www.bibsonomy.org/bibtex/2e73974133ef69a38e3f1de8834be245a/cdifsinarbonnecdifsinarbonne2008-06-25T00:48:02+02:00soignant prendre-soin soin soigné relation <span style="color:#555555;">Walter <a href="http://www.bibsonomy.org/author/Hesbeen">Hesbeen</a> </span><em>Masson, </em><em>masson edition, </em>(<em>2000</em>) <em>Bibliogr. p. 191-195 . </em>Wed Jun 25 00:48:02 CEST 2008massonBibliogr. p. 191-195195Prendre soin à l'hopital Inscrire le soin infirmier dans une perspective soignante2000soignant prendre-soin soin soigné relation Par des témoignages, la sincérité, la réalité du vécu quotidien, l'auteur propose une réflexion claire et approfondie sur la notion de "faire des soins" et "prendre soin", qui fait de cet ouvrage un élément essentiel à la nécessaire évolution de l'organisation des lieux hospitaliers. Il se destine donc à tous ceux qui se soucient d'inscrire leurs actions dans une perspective soignante.Semantic feature production norms for a large set ...[Behav Res Methods. 2005] - PubMed Resulthttp://www.bibsonomy.org/bibtex/2936af12b025e37b0a6aac6bc103f58a3/hothohotho2008-05-08T12:17:01+02:00relation semantic ontology dataset toread grounding ol <span style="color:#555555;">K <a href="http://www.bibsonomy.org/author/McRae">McRae</a> and G S <a href="http://www.bibsonomy.org/author/Cree">Cree</a> and M S <a href="http://www.bibsonomy.org/author/Seidenberg">Seidenberg</a> and C <a href="http://www.bibsonomy.org/author/McNorgan">McNorgan</a> </span><em>Behav Res Methods</em><em>37(4):547-559</em><em>Nov2005. </em>Thu May 08 12:17:01 CEST 2008Behav Res MethodsNov4547-559Semantic feature production norms for a large set of living and nonliving things372005relation semantic ontology dataset toread grounding ol Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.Extracting semantic relations from query logshttp://www.bibsonomy.org/bibtex/26e45b65feffd1545c6dca62bf4b8f53d/chriskoernerchriskoerner2008-05-05T21:48:27+02:00query relation logs <span style="color:#555555;">Ricardo <a href="http://www.bibsonomy.org/author/Baeza-Yates">Baeza-Yates</a> and Alessandro <a href="http://www.bibsonomy.org/author/Tiberi">Tiberi</a> </span><em>KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page76--85. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Mon May 05 21:48:27 CEST 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining76--85Extracting semantic relations from query logs2007query relation logs Extracting semantic relations from query logshttp://www.bibsonomy.org/bibtex/28037a0e1ff2f6b1227d2b9f5c3468825/chriskoernerchriskoerner2008-05-05T19:07:55+02:00relation logs query extraction semantic <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Baeza-Yates">Baeza-Yates</a> and A. <a href="http://www.bibsonomy.org/author/Tiberi">Tiberi</a> </span><em>KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page76--85. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Mon May 05 19:07:55 CEST 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining76--85Extracting semantic relations from query logs2007relation logs query extraction semantic http://www.bibsonomy.org/bibtex/2600329a57279dec96536bfb54b28c0da/smichasmicha2008-04-23T22:05:04+02:00relation Nonlinear <span style="color:#555555;">Roger D. <a href="http://www.bibsonomy.org/author/Huang">Huang</a> and Charles S. Y. <a href="http://www.bibsonomy.org/author/Lin">Lin</a> </span><em>Journal of Empirical Finance</em><em>3(4):347--368</em><em>Dec1996. </em>Wed Apr 23 22:05:04 CEST 2008Journal of Empirical FinanceDec4347--368An analysis of nonlinearities in term premiums and forward rates31996relation Nonlinear http://www.bibsonomy.org/bibtex/20b5da717cccf61926a98405e6148b81c/smichasmicha2008-04-23T22:05:04+02:00Uncertainty relation <span style="color:#555555;">Milos I. <a href="http://www.bibsonomy.org/author/Doroslovacki">Doroslovacki</a> </span><em>Signal Processing</em><em>81(2):409--418</em><em>Feb2001. </em>Wed Apr 23 22:05:04 CEST 2008Signal ProcessingFeb2409--418Uncertainty relations involving ordinary second moments in time and frequency for discrete-time signals812001Uncertainty relation http://www.bibsonomy.org/bibtex/2501a79dcc1d3969ec211740e40cfaff3/smichasmicha2008-04-22T15:17:45+02:00Volume-volatility relation <span style="color:#555555;">Kalok <a href="http://www.bibsonomy.org/author/Chan">Chan</a> and Wai-Ming <a href="http://www.bibsonomy.org/author/Fong">Fong</a> </span><em>Journal of Financial Economics</em><em>57(2):247--273</em><em>Aug2000. </em>Tue Apr 22 15:17:45 CEST 2008Journal of Financial EconomicsAug2247--273Trade size, order imbalance, and the volatility-volume relation572000Volume-volatility relation Physica Ahttp://www.bibsonomy.org/bibtex/21d8b03f1b59aa9e07d7aabf89026973f/smichasmicha2008-04-22T10:36:30+02:00Entropic relation uncertainty <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Zozor">Zozor</a> and C. <a href="http://www.bibsonomy.org/author/Vignat">Vignat</a> </span><em>Physica A: Statistical Mechanics and its Applications</em><em>375(2):499--517</em><em>Mar2007. </em>Tue Apr 22 10:36:30 CEST 2008Physica A: Statistical Mechanics and its ApplicationsMar2499--517On classes of non-Gaussian asymptotic minimizers in entropic uncertainty principles3752007Entropic relation uncertainty 01Physica Ahttp://www.bibsonomy.org/bibtex/259b66c44aa7aa1ffe30633b5935aac28/smichasmicha2008-04-22T10:36:30+02:00work Non-equilibrium relation <span style="color:#555555;">I. <a href="http://www.bibsonomy.org/author/Santamar{\&#039;i}a-Holek">Santamar'ia-Holek</a> and A. <a href="http://www.bibsonomy.org/author/P{\&#039;e}rez-Madrid">P'erez-Madrid</a> </span><em>Physica A: Statistical Mechanics and its Applications</em><em>387(7):1529--1537</em><em>Mar2008. </em>Tue Apr 22 10:36:30 CEST 2008Physica A: Statistical Mechanics and its ApplicationsMar71529--1537The non-equilibrium work relation: Thermodynamic analysis and microscopic foundations3872008work Non-equilibrium relation 01Physica Ahttp://www.bibsonomy.org/bibtex/2880693e3b582dc42394af34ab05f780d/smichasmicha2008-04-22T10:36:30+02:00output Input relation <span style="color:#555555;">Giulio <a href="http://www.bibsonomy.org/author/Bottazzi">Bottazzi</a> and Marco <a href="http://www.bibsonomy.org/author/Grazzi">Grazzi</a> and Angelo <a href="http://www.bibsonomy.org/author/Secchi">Secchi</a> </span><em>Physica A: Statistical Mechanics and its Applications</em><em>355(1):95--102</em><em>Sep2005. </em>Tue Apr 22 10:36:30 CEST 2008Physica A: Statistical Mechanics and its ApplicationsSep195--102Input output scaling relations in Italian manufacturing firms3552005output Input relation 01Economics Lettershttp://www.bibsonomy.org/bibtex/2f48409a4d4e0790c59f5d7952324b4d0/smichasmicha2008-04-21T22:09:52+02:00relation Neo-Malthusian <span style="color:#555555;">Chong K. <a href="http://www.bibsonomy.org/author/Yip">Yip</a> and Junxi <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> </span><em>Economics Letters</em><em>52(3):319--324</em><em>Sep1996. </em>Mon Apr 21 22:09:52 CEST 2008Economics LettersSep3319--324Population growth and economic growth: A reconsideration521996relation Neo-Malthusian Economics Lettershttp://www.bibsonomy.org/bibtex/2d9aeef3e54e175a6d7cec84a6ab925fe/smichasmicha2008-04-21T22:09:52+02:00order relation Stochastic <span style="color:#555555;">Wendelin <a href="http://www.bibsonomy.org/author/Schnedler">Schnedler</a> </span><em>Economics Letters</em><em>75(2):171--178</em><em>Apr2002. </em>Mon Apr 21 22:09:52 CEST 2008Economics LettersApr2171--178The virtue of being underestimated: a note on discriminatory contracts in hidden information models752002order relation Stochastic http://www.bibsonomy.org/bibtex/282a01cc139417148087f7847a1885a4e/jiljil2008-01-21T01:13:49+01:00learning relation semantic sat text <span style="color:#555555;">Peter D. <a href="http://www.bibsonomy.org/author/Turney">Turney</a> and Michael L. <a href="http://www.bibsonomy.org/author/Littman">Littman</a> </span><em>Machine Learning</em><em>60(1-3):251-278</em>(<em>2005</em>)Mon Jan 21 01:13:49 CET 2008Machine Learning1-3251-278Corpus-based Learning of Analogies and Semantic Relations.602005learning relation semantic sat text http://www.bibsonomy.org/bibtex/2ed9bfa78fb479dfad96568b4a68be0d1/savinovsavinov2008-01-17T17:57:19+01:00projection data identity relation inclusion entity nested semantics concept-oriented de-projection set concept programming ordering class model ordered <span style="color:#555555;">Alexandr <a href="http://www.bibsonomy.org/author/Savinov">Savinov</a> </span><em>Technical Report RT0006, </em><em>Institute of Mathematics and Computer Science, Academy of Sciences of Moldova, </em>(<em>2007</em>)Thu Jan 17 17:57:19 CET 200840Two-Level Concept-Oriented Data ModelTechnical Report RT00062007projection data identity relation inclusion entity nested semantics concept-oriented de-projection set concept programming ordering class model ordered In this paper we describe a new approach to data modelling called the concept-oriented model (CoM). This model is based on the formalism of nested ordered sets which uses inclusion relation to produce hierarchical structure of sets and ordering relation to produce multi-dimensional structure among its elements. Nested ordered set is defined as an ordered set where an each element can be itself an ordered set. Ordering relation in CoM is used to define data semantics and operations with data such as projection and de-projection. This data model can be applied to very different problems and the paper describes some its uses such grouping with aggregation and multi-dimensional analysis.http://www.bibsonomy.org/bibtex/2742f262e0c7df4094073369b82ece723/savinovsavinov2008-01-17T17:54:04+01:00semantics property concept concept-oriented subconcept syntax relationship subitem inclusion aggregation characterisation inference primitive relation dimension item schema bottom top grouping superitem superconcept model <span style="color:#555555;">Alexandr <a href="http://www.bibsonomy.org/author/Savinov">Savinov</a> </span><em>Technical Report, </em><em>Institute of Mathematics and Computer Science, Academy of Sciences of Moldova, </em>(<em>2004</em>)Thu Jan 17 17:54:04 CET 200854Principles of the Concept-Oriented Data ModelTechnical Report2004semantics property concept concept-oriented subconcept syntax relationship subitem inclusion aggregation characterisation inference primitive relation dimension item schema bottom top grouping superitem superconcept model In the paper a new approach to data representation and manipulation is described, which is called the concept-oriented data model (CODM). It is supposed that items represent data units, which are stored in concepts. A concept is a combination of superconcepts, which determine the concept's dimensionality or properties. An item is a combination of superitems taken by one from all the superconcepts. An item stores a combination of references to its superitems. The references implement inclusion relation or attribute-value relation among items. A concept-oriented database is defined by its concept structure called syntax or schema and its item structure called semantics. The model defines formal transformations of syntax and semantics including the canonical semantics where all concepts are merged and the data semantics is represented by one set of items. The concept-oriented data model treats relations as subconcepts where items are instances of the relations. Multi-valued attributes are defined via subconcepts as a view on the database semantics rather than as a built-in mechanism. The model includes concept-oriented query language, which is based on collection manipulations. It also has such mechanisms as aggregation and inference based on semantics propagation through the database schema.http://www.bibsonomy.org/bibtex/24b27ee077a3fc147fcd8632dc807ac09/savinovsavinov2008-01-17T17:52:09+01:00concept-oriented model inclusion relationship constraint top schema superconcept bottom relation propagation inference primitive syntax dimension subconcept item aggregation characterisation superitem semantics property subitem concept grouping <span style="color:#555555;">Alexandr <a href="http://www.bibsonomy.org/author/Savinov">Savinov</a> </span><em>Computer Science Journal of Moldova</em><em>14(2):219--238</em>(<em>2006</em>)Thu Jan 17 17:52:09 CET 2008Computer Science Journal of Moldova2219--238Query by Constraint Propagation in the Concept-Oriented Data Model142006concept-oriented model inclusion relationship constraint top schema superconcept bottom relation propagation inference primitive syntax dimension subconcept item aggregation characterisation superitem semantics property subitem concept grouping The paper describes an approach to query processing in the concept-oriented data model. This approach is based on imposing constraints and specifying the result type. The constraints are then automatically propagated over the model and the result contains all related data items. The simplest constraint propagation strategy consists of two steps: propagating down to the most specific level using de-projection and propagating up to the target concept using projection. A more complex strategy described in the paper may consist of many de-projection/projection steps passing through some intermediate concepts. An advantage of the described query mechanism is that it does not need any join conditions because it uses the structure of the model for propagation. Moreover, this mechanism does not require specifying an access path using dimension names. Thus even rather complex queries can be expressed in simple and natural form because they are expressed by specifying what information is available what related data we want to get.http://www.bibsonomy.org/bibtex/217924e4426a88a55c12154b5b70676e0/iswc2007iswc20072007-11-07T19:13:58+01:00research_04 web_annotation information_extraction iswc semantic_web extraction 2007 relation wikipedia natural_language_processing text <span style="color:#555555;">Gang <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and Yong <a href="http://www.bibsonomy.org/author/Yu">Yu</a> and Haiping <a href="http://www.bibsonomy.org/author/Zhu">Zhu</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, </em><em>volume4825ofLNCS, </em><em>page575--588. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em><em>November2007. </em>Wed Nov 07 19:13:58 CET 2007Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember575--588LNCSPORE: Positive-Only Relation Extraction from Wikipedia Text48252007research_04 web_annotation information_extraction iswc semantic_web extraction 2007 relation wikipedia natural_language_processing text Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.http://www.bibsonomy.org/bibtex/27a6939700fc4fb08b728c72df52552bb/brainmachinebrainmachine2007-10-24T12:16:49+02:00temporal interval-based relation time knowledge <span style="color:#555555;">James F. <a href="http://www.bibsonomy.org/author/Allen">Allen</a> </span><em>Communications of the ACM</em>(<em>1983</em>)Wed Oct 24 12:16:49 CEST 2007{Communications of the ACM}832--843{Maintaining Knowledge about Temporal Intervals}261983temporal interval-based relation time knowledge citeulikehttp://www.bibsonomy.org/bibtex/20393d7380432e13aefe836e7242f2624/a_olympiaa_olympia2007-08-18T13:22:24+02:00recognition probabilistic relation entity <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Roth">Roth</a> and W. <a href="http://www.bibsonomy.org/author/Yih">Yih</a> </span>(<em>2002</em>)Sat Aug 18 13:22:24 CEST 2007Probabilistic reasoning for entity \& relation recognition2002recognition probabilistic relation entity This paper develops a method for recognizing relations and entities in sentences, while taking mutual dependencies among them into account. E.g., the kill (Johns, Oswald) relation in: {"}J. V. Oswald was murdered at JFK after his assassin, K. F. Johns...{"} depends on identifying Oswald and Johns as people, JFK being identified as a location, and the kill relation between Oswald and Johns; this, in turn, enforces that Oswald and Johns are people.