A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval

Web-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e., gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.

[1]  Albert Atkin.  Peirce’s Theory of Signs, by Thomas L. Short. , 2010 .

[2]  Lotfi A. Zadeh,et al.  Fuzzy probabilities , 1996, Inf. Process. Manag..

[3]  Alexander Klapproth,et al.  Prometheus — Fuzzy information retrieval for semantic homes and environments , 2010, 3rd International Conference on Human System Interaction.

[4]  Steven A. Sloman,et al.  The Problem of Induction , 2005 .

[5]  C. Allen,et al.  Stanford Encyclopedia of Philosophy , 2011 .

[6]  Nick Craswell,et al.  Methods for Distributed Information Retrieval , 2000 .

[7]  Marc Najork,et al.  Mercator: A scalable, extensible Web crawler , 1999, World Wide Web.

[8]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[9]  William J. Rapaport,et al.  What Did You Mean by That? Misunderstanding, Negotiation, and Syntactic Semantics , 2003, Minds and Machines.

[10]  Jay Zeman,et al.  Peirce ’ s Theory of Signs , 2014 .

[11]  Michael Kaufmann,et al.  Inductive Fuzzy Classification in Marketing Analytics , 2014 .

[12]  Thomas R. Gruber,et al.  Collective knowledge systems: Where the Social Web meets the Semantic Web , 2008, J. Web Semant..

[13]  T. Pavlidis,et al.  Fuzzy sets and their applications to cognitive and decision processes , 1977 .

[14]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[15]  R. Carnap,et al.  On Inductive Logic , 1945, Philosophy of Science.

[16]  Abraham Silberschatz,et al.  HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads , 2009, Proc. VLDB Endow..

[17]  Thomas L Griffiths,et al.  Rethinking language: How probabilities shape the words we use , 2011, Proceedings of the National Academy of Sciences.

[18]  M. Kaufmann,et al.  A concept of semantics extraction from web data by induction of fuzzy ontologies , 2013, IEEE International Conference on Electro-Information Technology , EIT 2013.

[19]  Sadaaki Miyamoto,et al.  Algorithms for Fuzzy Clustering - Methods in c-Means Clustering with Applications , 2008, Studies in Fuzziness and Soft Computing.

[20]  Ben Shneiderman,et al.  Designing The User Interface , 2013 .

[21]  T. L. Short.  Peirce's Theory of Signs , 2007 .

[22]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[23]  Martin Hilbert,et al.  The World’s Technological Capacity to Store, Communicate, and Compute Information , 2011, Science.

[24]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[25]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[26]  A. Maslow.  A Theory of Human Motivation , 1943 .

[27]  Gary Marchionini,et al.  Exploratory search , 2006, Commun. ACM.

[28]  Stevan Harnad.  The Symbol Grounding Problem , 1999, ArXiv.

[29]  Carl G. Hempel,et al.  I.—STUDIES IN THE LOGIC OF CONFIRMATION (II.) , 1945 .

[30]  L. Zadeh.  Calculus of fuzzy restrictions , 1996 .

[31]  P. Kleingeld,et al.  The Stanford Encyclopedia of Philosophy , 2013 .

[32]  Peter Ingwersen,et al.  Information Retrieval Interaction , 1992 .

[33]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[34]  L. Zadeh.  From Computing with Numbers to Computing with Words , 2001 .

[35]  Thomas L. Griffiths,et al.  Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling , 2009, NIPS.

[36]  G. Harman,et al.  The Problem of Induction , 2006 .

[37]  Rajesh P. N. Rao.  Hierarchical Bayesian Inference in Networks of Spiking Neurons , 2004, NIPS.