Building a Hierarchical, Granular Knowledge Cube

Authors: Alexander Denzler, Marcel Wehrle, Andreas Meier

Abstract:

A knowledge base stores facts and rules about the world that applications can use for the purpose of reasoning. By applying the concept of granular computing to a knowledge base, several advantages emerge. These can be harnessed by applications to improve their capabilities and performance. In this paper, the concept behind such a construct, called a granular knowledge cube, is defined, and its intended use as an instrument that manages to cope with different data types and detect knowledge domains is elaborated. Furthermore, the underlying architecture, consisting of the three layers of the storing, representing, and structuring of knowledge, is described. Finally, benefits as well as challenges of deploying it are listed alongside application types that could profit from having such an enhanced knowledge base.

Keywords: Granular computing, granular knowledge, hierarchical structuring, knowledge bases.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107720

References:

[1] E. Achtert, C. Böhm, H-P. Kriegel, P. Krüger, I. Müller-Gorman, A. Zimek, Finding Hierarchies of Subspace Clustering, Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD), Germany, pp. 446-453, 2008.
[2] C.C. Aggarwal, A. Hinneburg, D.A. Keim, On the Surprising Behavior of Distance Metrics in High Dimensional Space, In: Lecture Notes in Computer Science, Volume 1973, pp 420-434, 2001
[3] R. Angles, C. Gutierrez, Survey of graph databases, ACM Computing Surveys (CSUR) Volume 40, Issue 1, Article 1, pp.1-39, 2008.
[4] D.P. Ausubel, J. Novak, H. Hanesian,Educational Psychology: A Cognitive View, 2nd Edition, Rinehart & Winston, New York, pp. 251-257, 1978.
[5] A. Chan, E. Pampalk, Growing hierarchical self organizing map (GHSOM) toolbox: visualizations and enhancements, In: Neural Information Processing, 2002. ICONIP ’02. Proceedings of the 9th International Conference, Volume 5, pp. 2537-2541, 2002
[6] A.M. Collins, M.R. Quillian, Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, volume 8, pp. 240-248, (1969).
[7] X.L. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, W. Zhang, Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion, In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 601-610, 2014.
[8] J. Hey, The Data, Information, Knowledge, Wisdom Chain: The Metaphorical Link, pp. 4-8, 2004.
[9] S. Jouili, V. Vansteenberghe, An Empirical comparison of graph databases, IEEE, Social Computing (SOcialCom), pp. 708-715, 2013.
[10] J. Lampinen, E. Oja, Clustering Properties of Hierarchical Self-Organizing Maps,Journal of Mathematical Imaging and Vision, pp.261-272, 1992.
[11] H. Liu, H. Motoda, Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Group, USA, 1998.
[12] M. Minsky: A Framework for Representing Knowledge, MIT-AI Laboratory Memo 306, 1974.
[13] M. Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind, Simon & Schuster, Inc., 2006.
[14] G. Palla, I. Derényi, I. Farkas, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature 435, pp. 814-818, 2005.
[15] S. Puri, A Fuzzy Similarity Based Concept Mining Model for Text Classification, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 11, 2011.
[16] R. A. Quilian, A notation for representing conceptual information: An application to semantics and mechanical English paraphrasing, SP-1395, System Development Corporation, Santa Monica, 1963.
[17] M.R. Quilian, Semantic memory, In: Semantic Information Processing (Ed. M. Minsky), Cambridge, MA: MIT Press, pp. 227-270, 1975.
[18] A-B. M. Salem, M. Alfonse, Ontology versus Semantic Networks for Medical Knowledge Representation, 12th WSEAS International Conference on Computers, pp.768-774, 2008.
[19] U. Sattler, D. Calvanese, R. Molitor, Relationships with other formalisms, Description of logic handbook, pp. 137-177, 2003.
[20] B. Shao, H. Wang, Y. Xiao, Managing and mining large graphs: Systems and implementations, in proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD 12, pp. 589-592, New York, USA, 2012.
[21] J. F. Sowa, Conceptual Graphs for a Data Base Interface, IBM Journal of Research and Development 20 (4), pp. 336-357, 1976.
[22] G Weikum, M. Theobald, From information to knowledge: harvesting entities and relationships from web sources, In: Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, ACM, pp. 65-76, 2010.
[23] W. Wu, H. Li, H. Wang, K.Q. Zhu. Probase: A probabilistic taxonomy for text understanding, SIGMOD, ACM, pp. 481-492, 2012.
[24] W3C, OWL Web Ontology Language Overview, 2004.
[25] W3C, Resource Description Framework (RDF) Schema, 1998.
[26] Y.Y. Yao, B. Zhou, A logic language of granular computing, Proceedings of the 6th IEEE International Conference on Cognitive Informatics, IEEE Press, pp. 178-185, 2007.
[27] Y.Y. Yao, The Art of granular computing, Proceedings of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms, LNAI 4585, Springer, pp. 101-112, 2007.
[28] L. Zadeh, Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, Volume 19, pp. 111-127, 1997.