Analysis of Metrics for Intelligent Information Systems

: cc. 96 - 111
Lviv Polytechnic National University

The problem of constructing metrics is crucial for solving the problem of quantitative evaluation of both systems of objects of arbitrary nature as a whole and the relationships that describe the connections between the components of these systems. Modern information systems simulate subject areas that contain objects and systems of complex structure. The network model is most appropriate for describing the world around it: it reflects objects and systems of objects of arbitrary nature that interact with each other. In fact, any system can be described using a network model. Hierarchical models should be singled out as a kind of network models of complex systems. Hierarchical models are very widespread and are used in various fields — in biology, sociology, economics, technology, management, etc. — each industry has a set of its own hierarchical models. The paper analyzes metrics suitable for evaluating intelligent information systems, in particular — systems that are based on ontologies, non-relational (hierarchical) databases, non-normalized (nested) relationships.

  1. Kolmogorov A. N., & Fomin S. V. (1976). Elements of the theory of functions and functional analysis. Nauka (Science) (original title: Колмогоров А. Н., & Фомин С. В. Элементы теории функций и функционального анализа. Наука).
  2. G. Choquet. (1953). Theory of capacities. Ann. Inst. Fourier (Grenoble), 5, 31-295.
  3. D. Denneberg. (1994). Non-Additive Measure and Integral. Dordrecht: Kluwer Academic Publishers.
  4. Ralph Kimball. (2013). Dimensional Modeling Techniques. Additive, Semi-Additive, and Non-Additive Facts. Kimball group.
  5. Mahalanobis, P. C. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49-55.
  6. Richard E. Blahut. (1983). Theory and practice of error control codes. Addison-Wesley.
  7. Maarten Grootendorst. (2021). 9 Distance Measures in Data Science. The advantages and pitfalls of common distance measures. Towards data science.
  8. Jaccard P. (1901). Distribution de la flore alpine dans le Bassin des Dranses et dans quelques regions voisines. Bull. Soc. Vaudoise sci. Natur, 37(140), 241-272.
  9. Sørensen T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. Kongelige Danske Videnskabernes Selskab. Biol. Krifter, V(4), 1-34.
  10. Dice, Lee R. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology, 26(3), 297-302. doi:10.2307/1932409. p
  11. Fenton N. E., Pfleeger S. L. (1997). Software Metrics: A Rigorous and Practical Approach. International Thompson Computer Press.
  12. Chris Naylor. (1983). Build your own PC expert system. Sigma Press.
  13. G. S. Tesler. (2005). Metrics and norms in the hierarchy of categorical semantics and functions. Mathematical machines and systems, 2, 65-68.
  14. Velychko V. Yu. (2004). Solving analytical problems in discrete media by inference methods by analogy. Ph.D. thesis, Institute of Cybernetics, Kyiv,
  15. V. Lytvyn. (2011). Knowledge base of intelligent decision support systems. Lviv Polytechnic Publishing House. ISBN 978-617-607-059-7.
  16. D. Dosyn, & V. Lytvyn, & Yu. Nikolsky, & V. Pasichnyk. (2009). Intelligent systems based on ontologies. Civilization, Lviv.
  17. V. Lytvyn. (2008). A method of entering metrics to determine the distance between text documents. Information systems and networks, 621, 162-171.
  18. V. Lytvyn, & V. Vysotska, & D. Dosyn, & O. Lozynska, & O. Oborska. (2018). Methods of building intelligent decision support systems based on adaptive ontology. Proceedings of the IEEE Second International Conference on Data Stream Mining & Processing. p
  19. Duy Hoa Ngo, & Zohra Bellahsene, & Remi Coletta. (2011). A Generic Approach for Combining Linguistic and Context Profile Metrics in Ontology Matching. ODBASE’2011: 10th International Conference on Ontologies, DataBases, and Applications of Semantics, Oct 2011, Crete, Greece, 800-807. p
  20. Alsayed Algergawy, & Samira Babalou, & Birgitta Konig-Ries. (2016). A New Metric To Evaluate Ontology Modularization. 2nd International Workshop on Summarizing and Presenting Entities and Ontologies Co-located with the 13th Extended Semantic Web Conference. Greece, 2016-05-30.
  21. Giorgos Stoilos, & Giorgos Stamou, & Stefanos Kollias. (2005). A String Metric for Ontology Alignment. International Semantic Web Conference ISWC 2005: The Semantic Web - ISWC, 624-637. p
  22. J. García, & F, J. García-Peñalvo, & R. Therón. (2010). A Survey on Ontology Metrics. World Summit on Knowledge Society WSKS 2010: Knowledge Management, Information Systems, E-Learning, and Sustainability Research. 22-27. p
  23. Denny Vrandecic, & York Sure. (2007). How to Design Better Ontology Metrics. In The Semantic Web: Research and Applications (pp. 311-325), Springer-Berlag. p
  24. Harith Alani, & Christopher Brewster, & Nigel Shadbolt. (2006). Ranking Ontologies with AKTiveRank. Proceedings of the International Semantic Web Conference, ISWC, 2006 5th International Semantic Web Conference (ISWC), November 2006, Georgia, USA. p
  25. Harith Alani, & Christopher Brewster. (2006). Metrics for Ranking Ontologies. 4th Int. EON Workshop, 15th Int. World Wide Web Conference.
  26. Anthony Orme, & Haining Yao, & Letha Etzkorn. (2006). Coupling Metrics for Ontology-Based Systems. IEEE Software, 102-108. p
  27. Haining Yao, & Anthony Orme, & Letha Etzkorn. (2005). Cohesion Metrics for Ontology Design and Application. Journal of Computer Science, 1(1). 107-113. p
  28. Yinglong Ma, & Beihong Jin, & Yulin Feng. (2009). Semantic oriented ontology cohesion metrics for ontology-based systems. The Journal of Systems and Software, Elsevier
  29. Nicola Guarino, & Chris Welty. (2004). An Overview of OntoClean. The Handbook on Ontologies. Berlin: Springer-Verlag,151-172. p
  30. Nicola Guarino, & Chris Welty. (2002). Evaluating Ontological Decisions with OntoClean. Communications of the ACM, ACM Press, 61-65,. p
  31. Zhe YANG, & Dalu Zhang, & Chuan YE. (2006). Evaluation Metrics for Ontology Complexity and Evolution Analysis. IEEE International Conference on e-Business Engineering (ICEBE’06). p
  32. Joe Raad, & Christophe Cruz. (2015). A Survey on Ontology Evaluation Methods. Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, At Lisbon, Portugal, November 2015. p
  33. Maedche, A., & Staab, S. (2002). Measuring similarity between ontologies. In Knowledge engineering and knowledge management: Ontologies and the semantic web (pp. 251-263). Springer Berlin Heidelberg. p
  34. Ponzetto, S. P., & Strube, M. (2007). Deriving a large scale taxonomy from Wikipedia. AAAI, 7, 1440-1445.
  35. Treeratpituk, P., & Khabsa, M., & Giles, C. L. (2013). Graph-based Approach to Automatic Taxonomy Generation (GraBTax). arXiv preprint arXiv:1307.1718.
  36. Elias Zavitsanos, & George Paliouras, & George A. Vouros. (2011). Gold standard evaluation of ontology learning methods through ontology transformation and alignment. IEEE Trans. on Knowl. and Data Eng., 23(11),1635-1648. p
  37. Kashyap, V., & Ramakrishnan, & C., Thomas, C., & Sheth, A. (2005). TaxaMiner: an experimentation framework for automated taxonomy bootstrapping. International Journal of Web and Grid Services, 1(2), 240-266. p
  38. Dagobert Soergel, & Olivia Helfer. (2016). A Metrics Ontology. An intellectual infrastructure for defining, managing, and applying metrics. Knowl Organ Sustain World Chall Perspect Cult Sci Technol Shar Connect Soc., 15, 333-341.