Formal Concept Discovery in Semantic Web Data

Semantic Web efforts aim to bring the WWW to a state in which all its content can be interpreted by machines; the ultimate goal being a machine-processable Web of Knowledge. We strongly believe that adding a mechanism to extract and compute concepts from the Semantic Web will help to achieve this vision. However, there are a number of open questions that need to be answered first. In this paper we will establish partial answers to the following questions: 1) Is it feasible to obtain data from the Web (instantaneously) and compute formal concepts without a considerable overhead; 2) have data sets, found on the Web, distinct properties and, if so, how do these properties affect the performance of concept discovery algorithms; and 3) do state-of-the-art concept discovery algorithms scale wrt. the number of data objects found on the Web?

[1]  Alexey Neznanov,et al.  Comparing and analyzing the computational complexity of FCA algorithms , 2010, SAICSIT '10.

[2]  Enrico Motta,et al.  Extracting relevant questions to an RDF dataset using formal concept analysis , 2011, K-CAP '11.

[3]  Sébastien Ferré,et al.  Conceptual Navigation in RDF Graphs with SPARQL-Like Queries , 2010, ICFCA.

[4]  Roy T. Fielding,et al.  Principled design of the modern Web architecture , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[5]  Vilém Vychodil,et al.  Advances in Algorithms Based on CbO , 2010, CLA.

[6]  Julien Tane,et al.  Query-Based Multicontexts for Knowledge Base Browsing: An Evaluation , 2006, ICCS.

[7]  Uta Priss.  Formal concept analysis in information science , 2006 .

[8]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[9]  Simon Polovina,et al.  Conceptual Structures for Discovering Knowledge , 2011, Lecture Notes in Computer Science.

[10]  Steffen Staab,et al.  Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis , 2005, J. Artif. Intell. Res..

[11]  Johanna Völker,et al.  Lexico-logical acquisition of OWL DL axioms: an integrated approach to ontology refinement , 2008, ICFCA 2008.

[12]  Ke xin Jia,et al.  Binary Morphology based Kernelized Possibilistic C-means Algorithm , 2011 .

[13]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[14]  Pascal Hitzler,et al.  Conceptual Structures: Inspiration and Application, 14th International Conference on Conceptual Structures, ICCS 2006, Aalborg, Denmark, July 16-21, 2006, Proceedings , 2006, ICCS.

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

[16]  Jan Outrata,et al.  Parallel Recursive Algorithm for FCA , 2008 .

[17]  Ming Che Lee,et al.  FCA based concept constructing and similarity measurement algorithms , 2010, 2010 6th International Conference on Advanced Information Management and Service (IMS).

[18]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[19]  G Stix,et al.  The mice that warred. , 2001, Scientific American.

[20]  Simon Andrews.  In-Close2, a High Performance Formal Concept Miner , 2011, ICCS.

[21]  Anna Formica,et al.  Concept similarity in Formal Concept Analysis: An information content approach , 2008, Knowl. Based Syst..

[22]  J. Carroll,et al.  Jena: implementing the semantic web recommendations , 2004, WWW Alt. '04.

[23]  Bernhard Ganter,et al.  Formal Concept Analysis , 2013 .

[24]  Douglas Crockford,et al.  The application/json Media Type for JavaScript Object Notation (JSON) , 2006, RFC.

[25]  Sergei O. Kuznetsov,et al.  Comparing performance of algorithms for generating concept lattices , 2002, J. Exp. Theor. Artif. Intell..