First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

The development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques.

[1]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[2]  Alfred V. Aho,et al.  The Transitive Reduction of a Directed Graph , 1972, SIAM J. Comput..

[3]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[4]  Bernard Monjardet,et al.  Metrics on partially ordered sets - A survey , 1981, Discret. Math..

[5]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[6]  Ulrich Endriss,et al.  Bidding Languages and Winner Determination for Mixed Multi-unit Combinatorial Auctions , 2007, EUMAS.

[7]  Salvatore J. Bavuso,et al.  Dynamic fault-tree models for fault-tolerant computer systems , 1992 .

[8]  Henry A. Kautz,et al.  Extending Continuous Time Bayesian Networks , 2005, AAAI.

[9]  Suchi Saria,et al.  Reasoning at the Right Time Granularity , 2007, UAI.

[10]  Craig Boutilier,et al.  Bidding Languages for Combinatorial Auctions , 2001, IJCAI.

[11]  Karin M. Verspoor,et al.  Exploiting Term Relations for Semantic Hierarchy Construction , 2008, 2008 IEEE International Conference on Semantic Computing.

[12]  Uffe Kjærulff,et al.  dHugin: a computational system for dynamic time-sliced Bayesian networks , 1995 .

[13]  Cliff Joslyn,et al.  Evaluating the Structural Quality of Semantic Hierarchy Alignments , 2008, SEMWEB.

[14]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[15]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[16]  Cliff Joslyn,et al.  Evaluating the Structural Quality of Semantic Hierarchy Alignments , 2008, International Semantic Web Conference.

[17]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[18]  Ben Taskar,et al.  Probabilistic Relational Models , 2014, Encyclopedia of Social Network Analysis and Mining.

[19]  Yu Fan,et al.  Sampling for Approximate Inference in Continuous Time Bayesian Networks , 2008, ISAIM.

[20]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[21]  Karin M. Verspoor,et al.  A categorization approach to automated ontological function annotation , 2006, Protein science : a publication of the Protein Society.

[22]  Allen Newell,et al.  Chunking in Soar: The anatomy of a general learning mechanism , 1985, Machine Learning.

[23]  Brian A. Davey,et al.  An Introduction to Lattices and Order , 1989 .

[24]  Madalina Croitoru,et al.  BIDFLOW: a New Graph-Based Bidding Language for Combinatorial Auctions , 2008, ECAI.

[25]  Ben Taskar,et al.  Markov Logic: A Unifying Framework for Statistical Relational Learning , 2007 .

[26]  Luc De Raedt,et al.  Bayesian Logic Programming: Theory and Tool , 2007 .

[27]  Brian McBride,et al.  Jena: A Semantic Web Toolkit , 2002, IEEE Internet Comput..

[28]  Ulrich Endriss,et al.  Winner determination for mixed multi-unit combinatorial auctions via petri nets , 2007, AAMAS '07.

[29]  Herbert A. Simon,et al.  The Processes of Scientific Discovery: The Strategy of Experimentation , 1988, Cogn. Sci..

[30]  Daphne Koller,et al.  Expectation Propagation for Continuous Time Bayesian Networks , 2005, UAI.

[31]  Cliff Joslyn,et al.  Valuations and Metrics on Partially Ordered Sets , 2009, ArXiv.

[32]  Graeme Hirst,et al.  Evaluating WordNet-based Measures of Lexical Semantic Relatedness , 2006, CL.

[33]  Thomas Schiex,et al.  An Algebraic Graphical Model for Decision with Uncertainties, Feasibilities, and Utilities , 2007, J. Artif. Intell. Res..

[34]  Nir Friedman,et al.  Gibbs Sampling in Factorized Continuous-Time Markov Processes , 2008, UAI.

[35]  John E. Laird,et al.  Extending the Soar Cognitive Architecture , 2008, AGI.

[36]  Martin Ester,et al.  Join Bayes Nets: A new type of Bayes net for relational data , 2008, ArXiv.

[37]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[38]  Cliff Joslyn,et al.  The Gene Ontology Categorizer , 2004, ISMB/ECCB.

[39]  Cristopher Moore,et al.  Structural Inference of Hierarchies in Networks , 2006, SNA@ICML.

[40]  Paul S. Rosenbloom,et al.  Towards a New Cognitive Hourglass: Uniform Implementation of Cognitive Architecture via Factor Graphs , 2009 .

[41]  David Parker,et al.  Symbolic Representations and Analysis of Large Probabilistic Systems , 2004, Validation of Stochastic Systems.

[42]  Joel Waldfogel,et al.  Introduction , 2010, Inf. Econ. Policy.

[43]  J. D. Uiiman,et al.  Principles of Database Systems , 2004, PODS 2004.

[44]  Paul S. Rosenbloom,et al.  A Cognitive Odyssey: From the Power Law of Practice to a General Learning Mechanism and Beyond , 2006 .

[45]  Alex Kulesza,et al.  TBBL: A Tree-Based Bidding Language for Iterative Combinatorial Exchanges , 2005 .

[46]  Pedro M. Domingos,et al.  A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC , 2008, AAAI.

[47]  Robert W. Day,et al.  Expressing Preferences with Price-Vector Agents in Combinatorial Auctions , 2004 .

[48]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[49]  Jon Doyle,et al.  A Truth Maintenance System , 1979, Artif. Intell..

[50]  Allen Newell,et al.  A Universal Weak Method: Summary of Results , 1983, IJCAI.

[51]  Richard Reviewer-Granger Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[52]  Cliff Joslyn,et al.  Automated Annotation-Based Bio-Ontology Alignment with Structural Validation , 2009 .

[53]  Daphne Koller,et al.  Continuous Time Bayesian Networks , 2012, UAI.

[54]  Andrew S. Miner Decision diagrams for the exact solution of Markov models , 2007 .

[55]  X. Jin Factor graphs and the Sum-Product Algorithm , 2002 .

[56]  Ben Taskar,et al.  Selectivity estimation using probabilistic models , 2001, SIGMOD '01.

[57]  Yoav Shoham,et al.  Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches , 1999, IJCAI.

[58]  Jean-Gabriel Ganascia,et al.  Using AI to Reconstruct Claude Bernard's Empirical Investigations , 2008, IC-AI.

[59]  David Maxwell Chickering,et al.  Finding Optimal Bayesian Networks , 2002, UAI.

[60]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[61]  Eljas Soisalon-Soininen,et al.  On Finding the Strongly Connected Components in a Directed Graph , 1994, Inf. Process. Lett..

[62]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[63]  Stuart J. Russell,et al.  BLOG: Probabilistic Models with Unknown Objects , 2005, IJCAI.

[64]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[65]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.