Engels Rajangam; Chitra Annamalai

Graph Models for Knowledge Representation and Reasoning for Contemporary and Emerging Needs – A Survey

Full Text (PDF, 475KB), PP.14-22

Views: 0 Downloads: 0

Author(s)

Engels Rajangam ^1,* Chitra Annamalai ²

1. PSG College of Technology, Department of Computer Science and Engineering, Coimbatore, 641004, India

2. PSG College of Technology, Department of Computer Applications, Coimbatore, 641004, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.02.02

Received: 2 Jun. 2015 / Revised: 14 Sep. 2015 / Accepted: 6 Nov. 2015 / Published: 8 Feb. 2016

Index Terms

Graph models, Knowledge Representation and Reasoning, Big Data, Concept Graphs, Semantic Networks, Inference Graphs, Causal Bayesian Networks

Abstract

Reasoning is the fundamental capability which requires knowledge. Various graph models have proven to be very valuable in knowledge representation and reasoning. Recently, explosive data generation and accumulation capabilities have paved way for Big Data and Data Intensive Systems. Knowledge Representation and Reasoning with large and growing data is extremely challenging but crucial for businesses to predict trends and support decision making. Any contemporary, reasonably complex knowledge based system will have to consider this onslaught of data, to use appropriate and sufficient reasoning for semantic processing of information by machines. This paper surveys graph based knowledge representation and reasoning, various graph models such as Conceptual Graphs, Concept Graphs, Semantic Networks, Inference Graphs and Causal Bayesian Networks used for representation and reasoning, common and recent research uses of these graph models, typically in Big Data environment, and the near future needs and challenges for graph based KRR in computing systems. Observations are presented in a table, highlighting suitability of the surveyed graph models for contemporary scenarios.

Cite This Paper

Engels Rajangam, Chitra Annamalai, "Graph Models for Knowledge Representation and Reasoning for Contemporary and Emerging Needs – A Survey", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.2, pp.14-22, 2016. DOI:10.5815/ijitcs.2016.02.02

Reference

[1]D. Agrawal et al, “Challenges and Opportunities with Big Data”, White Paper, Computing Community Consortium. (2012), http://cra.org/ccc/docs/init/ bigdatawhitepaper.pdf.

[2]J. F. Sowa, “Conceptual Graphs for a Data Base Interface”, IBM Journal of Research and Development vol. 20 (4), 1976, pp.336–357.

[3]J. F. Sowa, Conceptual Structures: Information Processing in Mind and Machine, Reading, Addison-Wesley, 1984.

[4]F. Vanharmelen, V. Lifschitz, and B. Porter, Handbook of Knowledge Representation, Elsevier Science, San Diego, 2007.

[5]G. Antoniou and F. Vanharmelen, A Semantic Web Primer, 2nd ed, The MIT Press, 2008.

[6]L. K. Dillon, and R. E. K. Stirewalt, “Inference graphs: a computational structure supporting generation of customizable and correct analysis components”, Software Engineering, IEEE Transactions, vol. 29 (2), 2003, pp.133-150.

[7]D. R. Schlegel, and S. C. Shapiro, “Inference Graphs: A Roadmap. In Matthew Klenk and John Laird, Eds. Presented at the Proceedings of the Second Annual Conference on Advances in Cognitive Systems, Poster Collection, Baltimore, MD, 2013.

[8]R. E. Neapolitan, Learning Bayesian Networks. Prentice-Hall, Inc., Upper Saddle River, 2003.

[9]S. Russell, and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed, Prentice Hall Press, Upper Saddle River, NJ, 2009.

[10]M. Chein, J. Aubert, and J. Baget, “Simple conceptual graphs and simple concept graphs”, Presented at the Proceedings of the International Conference on Computational Science, New York, NY, 2006.

[11]H. Amiri, A. A. Ahmad, M. Rahgozar, and F. Oroumchian, “Query Expansion Using Wikipedia Concept Graph”, Presented at the Proceedings of the International Conference on Information and Knowledge, Dubai, 2008.

[12]M. Chein, and M. Mugnier, Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs. Springer, 2009.

[13]Protégé, retrieved from http://protege.stanford.edu on 03 Dec 2014.

[14]HermiT OWL Reasoner, retrieved from http://hermit-reasoner.com on 03 Dec 2014.

[15]FaCT++ reasoner, retrieved from http://owl.cs.manchester.ac.uk/tools/fact/ on 03 Dec 2014.

[16]J. PEARL, Causality: Models, Reasoning and Inference, 2nd ed., Cambridge University Press, New York, NY, 2009.

[17]I. Copi, C. Cohen, and K. McMahon, Introduction to Logic, Pearson Education Limited, 2014.

[18]The Semantic Web Services Language (Swsl), retrieved from.http://www.daml.org/services/swsf/1.0/swsl/bridge.shtml, on 03 Dec 2014

[19]J. F. Sowa, “Conceptual graphs as a universal knowledge representation”, Computers & Mathematics with Applications, vol. 23, 75-93, 1992.

[20]G. Ellis et al, Conceptual Graphs, retrieved from http://conceptualgraphs.org on 15 Dec 2014

[21]Amine Platform, retrieved from http://sourceforge. net/projects/amine-platform/ and http://amine-platform. sourceforge.net/component/structures/CG.htm#Concept(API) on 13 Jan 2015.

[22]Cogitant, retrieved from http://cogitant.sourceforge.net/ on 13 Jan 2015.

[23]F. Southey, and J. G. Linders, “Notio - A Java API for Developing CG Tools”, Presented at the Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices, Blacksburg, VA, 1999.

[24]J. F. Sowa, “Conceptual Graph Summary”, http://www.jfsowa.com/cg/cgif.htm, retrieved on 15 Dec 2014.

[25]M. Croitoru, E. Compatangelo, and C. Mellish, “Hierarchical Knowledge Integration Using Layered Conceptual Graphs”, Presented at the Proceedings of the 13th International Conference on Conceptual Structures in Lecture Notes in Computer Science Series, Kassel, Germany, 2005.

[26]B. Kamsu-Foguem, G. Diallo, and C. Foguem, “Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine”, Engineering Applications of Artificial Intelligence, vol. 26, 1348-1365, 2013.

[27]CTakes 3.1.2 and YTEX, retrieved from https://cwiki.apache.org/confluence/display/CTAKES/cTAKES+3.1.2+-+Semantic+Similarity on 13 Jan 2015.

[28]Neo4j, retrieved from www.neo4j.com on 13 Jan 2015.

[29]Ondex, retrieved from http://www.ondex.org /api_manual.html on 13 Jan 2015

[30]R. Agrawal, S. Gollapudi, A. Kannan,and K. Kenthapadi, “Similarity Search using Concept Graphs”, Presented at the Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), New York, NY.

[31]J. P. Aubert, J. F. Baget, and M. Chein, “Simple Conceptual Graphs and Simple Concept Graphs”, Conceptual Structures: Inspiration and Application, vol. 4068, ISBN: 978-3-540-35893-0, 2006.

[32]T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web”, Scientific American Magazine, 2001.

[33]A. Abello, et al, “Using Semantic Web Technologies for Exploratory OLAP: A Survey”, IEEE Transactions on Knowledge and Data Engineering, vol. 27(2), 571-588, 2015.

[34]J. F. Sowa, “Future Directions for Semantic Systems”, retrieved from http://www.jfsowa.com /pubs/futures.pdf on 17 Jan 2015.

[35]D. R. Schlegel, and S. C. Shapiro, “Visually Interacting with a Knowledge Base Using Frames, Logic, and Propositional Graphs”, Presented in the Proceedings of the Second International Workshop, Berlin, Germany, 2012.

[36]D. R. Schlegel, and S. C. Shapiro, “Inference Graphs: A New Kind of Hybrid Reasoning System”, Presented at the Proceedings of the Cognitive Computing for Augmented Human Intelligence Workshop Quebec, Canada, 2014a.

[37]D. R. Schlegel, and S. C. Shapiro, “Concurrent Reasoning with Inference Graphs”, Presented at the Proceedings of the Second International Workshop on Graph Structures for KRR, Switzerland, 2014.

[38]K. Yu, W. Ding, H. Wang, and X. Wu, “Bridging Causal Relevance and Pattern Discriminability: Mining Emerging Patterns from High-Dimensional Data”, IEEE Transactions on Knowledge and Data Engineering, vol. 25(12), 2721-2739, 2013.

[39]OpenMarkov, retrieved from http:// www.openmarkov.org on 14 Jan 2015.

[40]GMTK, retrieved from http:// melodi.ee.washington.edu/gmtk/ on 15 Jan 2015.

[41]Open BUGS retrieved from http://www.openbugs.net on 15 Jan 2015.

[42]Bayes server, Bayes time series retrieved from http://www.bayesserver.com on 16 Jan 2015.

[43]Netica, retrieved from http://www.norsys.com /netica.html on 16 Jan 2015

[44]Bayes Networks, retrieved from http://www. bayesnets. com/ on 15 Jan 2015.

[45]R. Brachman, and H. Levesque, Knowledge Representation and Reasoning, Morgan Kaufmann Publishers Inc., San Francisco, 2004.

[46]A. Dennai, and S. M. Benslimane, “Semantic Indexing of Web Documents Based on Domain Ontology”, International Journal of Information Technology and Computer Science, 1-11, 2015. DOI: 10.5815/ijitcs.2015.02.01.

[47]A. S. Vijendran, and C. Deepa, “SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction”, International Journal of Information Technology and Computer Science, 41-47, 2014. DOI: 10.5815/ijitcs.2015.01.05.

International Journal of Information Technology and Computer Science (IJITCS)