Document Type : Articles


Faculty Member and Assistant Professor, Academic Relations and International Affairs (ARIA), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.


The following article proposes a novel context by Information Quanta in the structural analysis of ontologies, which could be used to develop ontology evaluation metrics and measures. The identification of information quantum needs to clarify knowledge quanta in knowledge systems and semantic networks by considering two influential theories as Quantum Theory of Knowledge (QTK) proposed by Burgin (1995a; 1997; 2004) and the Semantic Link Theory of Knowledge (SLTK) proposed by Zhuge (2004; 2010; 2012). QTK identifies the quantum level of knowledge as knowledge quanta comprising the minimal blocks or units of knowledge in the construction of knowledge systems. In this case, knowledge quanta are primitive propositions and predicates. Elementary units of semantic networks, the triad of two nodes and a labeled semantic link between them, in SLTK based on the Semantic Link Network Theory (SLNT) could be conceptualized as knowledge quanta. Quantum units of knowledge are shared representations in the QTK and the SLTK. As a kind of semantic network, ontology also includes the triads of nodes and semantic links defined as knowledge quanta here. A semantic link as a knowledge quantum can be divided into its components, i.e., into three parts: subject, object, and relation. These separate parts can be considered as the information quantum or semantic network data. Finally, it can be said that the information quantum or data in the semantic network of ontologies that include the subject, object, and relation are derived from the fragmentation of the semantic links into their components. Identifying information quanta in ontologies could play an influential role in establishing and developing a new context in the structural analysis of ontologies through proposing, developing and applying new metrics and criteria in measurement of the mentioned quantum elements (i.e., ontology data).


Amirhosseini, M. & Salim, J. (2019). A Synthesis Survey of Ontology Evaluation Tools, Applications and Methods to Propose a Novel Branch in Evaluating the Structure of Ontologies: Graph-independent Approach. International Journal of Computer (IJC), 33 (1), 46-68.
Amirhosseini, M. (2016). Analysis of concept structure and semantic relations based on graph-independent structural analysis. Ph. D. Dissertation. Faculty of Information Sciences and Technology, Universiti Kebangsaan Malaysia.
Burgin, M. (1995). The phenomenon of knowledge. Philosophical and Sociological Thought, No. 3(4), 41–63.
Burgin, M. (1997). Fundamental Structures of Knowledge and Information, Ukrainian Academy of Information Sciences, Kiev (in Russian). In Structures and Processes. New Jersey: World Scientific Series in Information Studies, pp. 45-168.
Burgin, M. (2004). Data, information, and knowledge. Information, 7 (1), 47–57.
Burgin, M. (2010). Theory of Information: Fundamentality, Diversity and Unification. New York/London/Singapore: World Scientific.
Burgin, M. (2012). Structural Reality. New York: Nova Science Publishers.
Burgin, M. (2017). Knowledge Structure and Functioning: Micro level or Quantum Theory of Knowledge. In Theory of Knowledge Structures and Processes. New Jersey: World Scientific Series in Information Studies, pp. 307-394.
Burgin, M. (2017a). Knowledge Characteristics and Typology. In Theory of Knowledge Structures and Processes. New Jersey: World Scientific Series in Information Studies, pp. 45-168.
Burgin, M. (2017b). Knowledge Structure and Functioning: Macro level or Theory of Average Knowledge. In Theory of Knowledge Structures and Processes. New Jersey: World Scientific Series in Information Studies, pp. 395-592.
Burridge, K. & Stebbins, T. N. (2020). For the Love of Language: An Introduction to Linguistics. London: Cambridge University press.
Caves, C. M., Fuchs, C. A. & Schack, R. (2002). Quantum probabilities as Bayesian probabilities. Physical Review. 56 (2), 1-6.
Chalmers, D. J. (1995). Facing up to the hard problem of consciousness. Journal of Consciousness Studies, 2(3), 200-219.
Chung, W. (2010). Web Searching and Browsing: A Multilingual Perspective. In Advances in Computers Volume, 78, ( pp: 41-69).
David, L. (2020). Data Engineering: What is it? A definition based in data and historical context. Towards Data Science. Retrieved from
De Silva, S. T. (2008). An Ontology to Model Time in Clinical Practice Guideline. Master of Health Informatics, Dalhousie University.
Frieden, R. B. (1998). Physics from Fisher Information. Cambridge: Cambridge University Press.
Gangemi, A., Catenacci, C., Ciaramita, M. & Lehmann, J.  (2005). A theoretical framework for ontology evaluation and validation. In Semantic Web Applications and Perspectives, Proceedings of the 2nd Italian Semantic Web Workshop, (pp. 14-16), Trento: University of Trento.
Gangemi, A., Catenacci, C., Ciaramita, M. & Lehmann, J. (2006). Modelling ontology evaluation and validation. In European Semantic Web Conference (pp. 1-15). Springer, Berlin, Heidelberg.
Gordon, T. C. (2004). Quantum Information Theory and the Foundations of Quantum Mechanics. Thesis or the degree of Doctor of Philosophy. Oxford: University of Oxford
Gupta, A. (2021). Some Foundational Issues in Quantum Information Science. Online First: IntechOpen, DOI: 10.5772/intechopen.98769. Available from:
Hayles, N. K. (1999). How We Became Post Human: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, IL: University of Chicago Press.
Hiley, B. J. (2004). Information, quantum theory and the brain. Advances in Consciousness Research58, 199-216.
Horri, A. (2008). An Introduction to Informology. Tehran: Dama: Ketabdar.
Jacobs, J. (2001). The dimensions of topic–comment. Linguistics, 39, 641–81.
Khrennikov, A. (2016) Social Laser: Action amplification by stimulated emission of social energy, Philosophical Transactions of the Royal Society A, 374, 1-13. Retrieved from
King, J. C. (1995). Structured propositions and complex predicates. Nous, 19, 516–535.
Kuroda, S. Y. (2005). Focusing on the matter of topic: A study of wa and ga in Japanese. Journal of east asian linguistics, 14(1), 1-58.
Locke, J. (1690). An Essay Concerning Human Understanding. London: The Baffet.
Lu, Q. (2006). OntoKBEval: A Support Tool for OWL Ontology evaluation. Master of computer science, Concordia University.
Mulder, D. H. (2004). Objectivity. In The Internet encyclopedia of philosophy. Retrieved from
Nielsen, M. A. (2010). Quantum computation and quantum information. Chuang, Isaac L. (10th anniversary ed.). Cambridge: Cambridge University Press.
Obrst, L., Ashpole, B., Ceusters, W., Mani, I., Steve, R. & Smith, B. (2007). The evaluation of ontologies: Toward improved semantic interoperability. Semantic Web, (pp. 139-158.), Berlin: Springer, Retrieved from
Russell, B. (1903). Principles of Mathematics. Cambridge University Press, Cambridge.
Sasse, H. J. (1987). The thetic/categorical distinction revisited. Linguistics, 25, 511–80.
Saussure, F. D. (1916) Nature of the Linguistic Sign. In Cours de linguistique g´en´erale, New York: McGraw Hill Education.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423. Retrieved from shannon1948.pdf
Simperl, E. (2009). Reusing ontologies on the Semantic Web: A feasibility study. Data & Knowledge Engineering, 68, 905–925. Retrieved from fileadmin/documents/articles/reusing_ontologies.pdf
Smuts, J. C. (1926). Holism and Evolution. New York: The Macmillan Company, 362.
Sowa, J. F. (2008). Conceptual Graphs. In Handbook of Knowledge Representation. London: Elsevier.
Stubbe, H. (1670). The Plus Ultra Reduced to a Non Plus. London, England.
Von Heusinger, K. (2002). Information structure and the partition of sentence meaning. Travaux du Cercle Linguistique de Prague/Prague Linguistic Circle Papers, ed. by E. Haji cov a, P. Sgall, J. Hana, & T. Hoskovec, 4, 275-305.
Welty, C. & Guarino, N. (2001). Supporting Ontological Analysis of Taxonomic Relationships. Data & Knowledge Engineering, 39 (1) 51-74. Retrieved from <>
Young, P. (1987). The Nature of Information. New York, NY: Praeger Publishers.
Zaliwski, A.S. (2011). Information – is it Subjective or Objective? triple, 9 (1), 77-92.
Zhuge, H. & Shi, X. (2003). Fighting epidemics in the information and knowledge age. IEEE Computer, 36 (10), 114–116.
Zhuge, H. & Shi, X. (2004). Toward the eco-grid: A harmoniously evolved interconnection environment. Communications of the ACM, 47 (9), 78–83.
Zhuge, H. & Sun, Y. (2010). The schema theory for semantic link network. Future Generation Computer Systems, 26 (3), 408–420.
Zhuge, H. & Xu, B. (2011). Basic operations, completeness and dynamicity of cyber physical socio semantic link network CPSocio-SLN. Con-currency and Computation: Practice and Experience, 23 (9), 924–939.
Zhuge, H. & Zhang, J. (2010). Topological centrality and its applications. Journal of the American Society for Information Science and Technology, 61 (9), 1824–1841.
Zhuge, H. (2004). China's e-science knowledge grid environment. IEEE Intelligent Systems, 19 (1), 13–17.
Zhuge, H. (2010). Interactive semantics. Artificial Intelligence, 174, 190–204.
Zhuge, H. (2012). The Knowledge Grid: Toward Cyber-Physical Society. Singapore: World Scientific Publishing Co.