BibSonomyburst/user/diego_ma/conceptual_graphsBibSonomy RSS feed for /user/diego_ma/conceptual_graphs2012-02-17T03:40:33+01:00http://www.bibsonomy.org/bibtex/2b07f65452b813353bd8d6cbe816ecdd5/diego_madiego_ma2007-12-14T02:48:28+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Yang">Gi-Chui Yang</a>, and <a href="/author/Oh">Jonathan Oh</a> </span><em>Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice, </em><em>page 476-481. </em><em>ACM Press, </em>(<em>1993</em>)Fri Dec 14 02:48:28 CET 2007Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice476-481Knowledge Acquisition and Retrieval Based on Conceptual Graphs1993conceptual_graphs http://www.bibsonomy.org/bibtex/251f6e367c2fa940d90d0dc7ddafe40e7/diego_madiego_ma2007-12-14T02:46:48+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Sowa">John F. Sowa</a> </span><em>Proc. ACL 1979, </em><em>page 39-44. </em>(<em>1979</em>)Fri Dec 14 02:46:48 CET 2007Proc. ACL 197939-44Semantics of Conceptual Graphs1979conceptual_graphs http://www.bibsonomy.org/bibtex/21ed283fd0d493cc689c4c0119f5bd6f4/diego_madiego_ma2007-12-14T02:44:28+01:00inf_retrieval conceptual_graphs <span class="authorEditorList"><a href="/author/Nicolas">Stéphane Nicolas</a>, <a href="/author/Moulin">Bernard Moulin</a>, and <a href="/author/Milenau">Guy W. Milenau</a> </span><em>Conceptual Structures for Knowledge Creation and Communication Proceedings ICCS 2003, </em><em>volume 2746 of LNAI, </em><em>Springer-Verlag, </em><em>Berlin Heidelberg, </em>(<em>2003</em>)Fri Dec 14 02:44:28 CET 2007Berlin HeidelbergConceptual Structures for Knowledge Creation and Communication (Proceedings ICCS 2003)362-377LNAISesei: A {CG}-Based Filter for Internet Search Engines27462003inf_retrieval conceptual_graphs The web faces a major contradiction: it has been created to present information to human beings but the increase of available information sources on the web makes it more difficult for people to find the most relevant documents when browsing through numerous pages of URLs returned by search engines. Classic indexing and retrieval techniques have an important limitation: the retrieved documents do not necessarily match the meaning of the user?s query. In order to address this issue, we designed a system capable of matching conceptual structures extracted from both the user?s query and documents retrieved by a classic search engine like Google. This article presents our fully functional application and provides details about its behavior. The application provides a new framework for broader experiments in the manipulation of conceptual structures extracted from natural language documents and is built upon research and tools developped by the CG community.http://www.bibsonomy.org/bibtex/2e729e2c218776ddf3df76191a0dcb33e/diego_madiego_ma2007-12-14T02:43:28+01:00conceptual_graphs inf_retrieval <span class="authorEditorList"><a href="/author/Mishne">Gilad Mishne</a> </span><em>University of Amsterdam, </em>(<em>2004</em>)Fri Dec 14 02:43:28 CET 2007Source Code Retrieval Using Conceptual Graphs2004conceptual_graphs inf_retrieval The sharp increase in the amount of easily accessible information in the last decade resulted in a growing amount of research regarding information extraction and retrieval. Inside the general information retrieval framework, specialized methods emerged for specific domains, trying to exploit features of the information in these domains to improve its accessibility. Source code ? documents written in a computer programming language ? is one of these domains. Source code is a form of structured data, data in which information is stored both in the structure and in the content; it is, however, different from other structured document domains both in the nature of the structure and the nature of the content. Retrieval of information from source code is crucial for large-scale software development and maintenance, and is recognized as a problem both by software developers and information retrieval researchers; it is a vast research area with multiple interests and various sub-tasks. This thesis focuses on one aspect of these: the usage of the structure of the code to improve the retrieval. Our approach for improving the retrieval from source code uses conceptual modeling of the code. We employ conceptual graphs ? a knowledge representation formalism ? to design a retrieval model for code that uses both its structure and its content. The model contains a formal definition of the problem, a method for representing the code as conceptual graphs, and procedures for ranking their similarity to a query. We evaluate our model and show that for the code retrieval task it performs better than standard information retrieval approaches. The associated complexity implications are analyzed and discussed, searching for a balance of computational costs and retrieval performance. Our main conclusion is that using the structure for retrieval of code improves retrieval results substantially, and further work in this direction can improve the results even more.http://www.bibsonomy.org/bibtex/217566a6d0f88a57139a3e9c6008f0149/diego_madiego_ma2007-12-14T02:41:14+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Kabbaj">Adil Kabbaj</a> </span><em>SourceForge, </em>(<em>2005</em>)Fri Dec 14 02:41:14 CET 2007Concept, Conceptual Relation, and Conceptual Graph ({CG})2005conceptual_graphs http://www.bibsonomy.org/bibtex/2b957ecbf85dc7bac9c9fa6ec238ef710/diego_madiego_ma2007-12-14T02:38:39+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Fargues">Jean Fargues</a>, <a href="/author/Landau">Marie-Claude Landau</a>, <a href="/author/Dugourd">Anne Dugourd</a>, and <a href="/author/Catach">Laurent Catach</a> </span><em>IBM Journal of Research Development</em> <em>30(1):70-79</em> (<em>1986</em>)Fri Dec 14 02:38:39 CET 2007IBM Journal of Research Development170-79Conceptual Graphs for Semantics and Knowledge Processing301986conceptual_graphs http://www.bibsonomy.org/bibtex/20642a2f1b49bac1a0462998f8408bd8d/diego_madiego_ma2007-12-14T02:38:12+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Delugach">Harry S. Delugach</a>, and <a href="/author/Hinke">Thomas H. Hinke</a> </span><em>Jour. Computing and Info. Tech.</em> <em>4(4):291-307</em> (<em>1994</em>)Fri Dec 14 02:38:12 CET 2007Jour. Computing and Info. Tech.4291-307Using Conceptual Graphs to Represent Database Inference Security Analysis41994conceptual_graphs This paper describes an approach to database inference analysis based on conceptual graphs. The database inference problem is briefly described. Previous approaches are summarized, followed by a presentation of our inference model, called AERIE. The notions of an inference target class and an inference method class are introduced with examples given. Conceptual graphs are introduced as our means of representing database inference knowledge, as a first step toward analyzing and detecting database inference problems. The classification of inference target classes and the use of conceptual graphs for database inference detection are two important contributions of this paper. Four examples are used to illustrate the approach. We discuss some interesting issues raised by this work, and offer conclusions and our plans for future research.http://www.bibsonomy.org/bibtex/263f8c49966d6846c6f01dd934634ae72/diego_madiego_ma2007-12-14T02:37:28+01:00inference conceptual_graphs <span class="authorEditorList"><a href="/author/Clark">Peter Clark</a>, and <a href="/author/Porter">Bruce Porter</a> </span><em>Proc. ICCS&#039;97, </em><em>volume 1257 of Lecture Notes in AI, </em><em>page 521-535. </em><em>Berlin, </em><em>Springer, </em>(<em>1997</em>)Fri Dec 14 02:37:28 CET 2007BerlinProc. ICCS'97521-535Lecture Notes in AIUsing Access Paths to Guide Inference with Conceptual Graphs12571997inference conceptual_graphs Conceptual Graphs (CGs) are a natural and intuitive notation for expressing first-order logic statements. However, the task of performing inferences with a large-scale CG knowledge base remains largely unexplored [...] in this paper we show how a method used elsewhere for achieving tractability --- namely the use of \emph{access paths} --- can be applied to conceptual graphs [...]http://www.bibsonomy.org/bibtex/26d596efc3c6ca2c403eaf04d12aa7282/diego_madiego_ma2007-12-14T02:35:32+01:00conceptual_graphs inference <span class="authorEditorList"><a href="/author/Antonacci">Francesco Antonacci</a>, and <a href="/author/Calamani">Cecilia M. Calamani</a> </span><em>IBM Journal of Research and Development</em> <em>36(3):333-345</em> (<em>1992</em>)Fri Dec 14 02:35:32 CET 2007IBM Journal of Research and Development3333-345Capturing the Deep Meaning of Texts through Deduction and Inference361992conceptual_graphs inference http://www.bibsonomy.org/bibtex/224d24f79aeaefa77af9a1833e8eb7428/diego_madiego_ma2007-12-14T02:35:11+01:00conceptual_graphs <span class="authorEditorList"><a href="/author/Agrawal">Rakesh Agrawal</a>, and <a href="/author/Srikant">Ramakrishnan Srikant</a> </span><em>Proc. 20th Int. Conf. Very Large Data Bases, VLDB, </em>(<em>1994</em>)Fri Dec 14 02:35:11 CET 2007Proc. 20th Int. Conf. Very Large Data Bases, VLDBFast Algorithms for Mining Association Rules1994conceptual_graphs We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Experiments with synthetic as well as real-life data show that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed ...