The use of ontological knowledge for multi-criteria comparison of complex information objects

J.V. Rogushina, A.Y. Gladun

Abstract


In this work we consider comparison of complex information objects (CIO) as a component of intelligent decision-making. The specifics of proposed approach is that we compare not all theoretically possible CIOs but only their subset that is relevant for cur- rent situation and contains existing and available objects. Thus, we find an acceptable solution from the set of available ones that can be not optimal (according to certain criteria). We propose formal ontology-based model of CIO that considered as an element of intellectual information system. This model (in contrast to the domain ontology) defines unique names for positions of classes and class individuals to indicate the SIO structure. The methods of CIO comparison based on the use of knowledge from the relevant do- main ontology are considered. Various approaches to determining of semantic proximity and semantic similarity are considered as metrics for quantitative evaluation to select parameters of information objects that can be used for calculation of these evaluations. We propose an algorithm for semantic comparison of CIOs which are based on the same ontology and have a similar structure. This algorithm allows generation of comparison criteria and determining hierarchy of this criteria for the current situation.

We propose to evaluate the semantic-level similarity of the elements of individual CIOs to certain reference CIO defined by the user (as a description of the optimal solution or generated CIO properties). As a result, a subset of CIOs that satisfy the user requirements is cre- ated, but we have to select only one CIO among them, which will be used in the future to fulfill the user task. Therefore, we need in a set of criteria for CIO comparison and methods to determine the importance of each of these criteria at the current moment in time. For this purpose, we propose to use the method of hierarchical analysis based on a pairwise comparison of the importance of individual criteria.

Prombles in programming 2022; 3-4: 249-259


Keywords


complex information object; ontology; semantic similarity; semantic proximity; analysis of hierarchies

References


PRYIMA, S. M.& STROKAN, O. V. & ROGUSHINA, J. V. et al. (2020). Ontological Analysis of Outcomes of Non-formal and Informal Learning for Agro-Advisory System AdvisOnt. International Conference on Technologies and Innovation, Springer, Cham. P. 3-17. Available from: https://link.springer.com/chapter/10.1007/978-3-030-62015-8_1. [Accessed 12/07/2022].

KOZYRKOV C. (2019). Introduction to Decision Intelligence. Available from: https://towardsdatascience.com/introduction-to-decision- intelligence-5d147ddab767 [Accessed 12/07/2022].

ANISIMOV A.V. & MARCHENKO O.O. & NIKONENKO A.O. (2008). Algorithmic model of associative-semantic contextual analysis of texts in natural language. Problems in Programming. No. 2-3, P. 379-384. Available from: https://core.ac.uk/reader/38468700. [Accessed 12/07/2022].(in Ukrainian).

KRYUKOV K.V. & PANKOVA L.A. & PRONINA V.A. and others (2010). Measures of semantic similarity in ontologies. Proc. of Scientific sessions MIFI-2010. Information and telecommunication systems. Problems of information security, vol. 5. S. 75-78. . (in Russian).

LASTRA-DÍAZ J. J. & GOIKOETXEA J. & TAIEB M.A.H. et al. (2019). A reproducible survey on word embeddings and ontology-based methods for word similarity: linear combinations outperform the state of the art. Engineering Applications of Artificial Intelligence, 85, P.645- 665. Available from: https://doi.org/10.1016/j.engappai.2019.07.010. [Accessed 11/07/2022].

LASTRA-DÍAZ J. & GARCÍA-SERRANO (2015). A. A new family of information content models with an experimental survey on WordNet. Knowledge-Based Systems, 89, P.509–526.

CAMACHO-COLLADOS J. & PILEHVAR M. (2018). T. From word to sense embeddings: A survey on vector representations of meaning. Journal of Artifcial Intelligence Research 63, P.743–788.

TAIEB M. A. H. & ZESCH T. & AOUICHA M. B. (2019). A survey of semantic relatedness evaluation datasets and procedures. Artifcial Intelligence Review 53(6). P. 4407–4448. Available from: https://doi.org/10.1007/s10462-019-09796-3. [Accessed 11/07/2022].

Altaśnel B. & Ganiz M. C. (2018). Semantic text classifcation: A survey of past and recent advances. Information Processing & Management 54, 6 (2018), P.1129 – 1153. https://doi.org/10.1016/j.ipm.2018.08.001. [Accessed 4/07/2022].

GAN M. & DOU X. & JIANG R. (2013). From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity. Sci. World J. , P.1–11.

SCHICKEL-ZUBER V. & FALTINGS B. (2007). OSS: A semantic similarity function based on hierarchical ontologies. Proc.of the IJCAI-07, India, P. 551–556.

MAEDCHE A. & STAAB S. (2002). Measuring similarity between ontologies. Proc.of the International Conference on Knowledge Engineering and Knowledge Management, P. 251–263.

CUNNINGHAM P. (2008). A Taxonomy of Similarity Mechanisms for Case-Based Reasoning. IEEE Trans. Knowl. Data Eng., 21, P.1532–1543.

LIAO T.W. & ZHANG Z. & MOUNT C.R. (1998). Similarity measures for retrieval in case-based reasoning systems. Appl. Artif. Intell., 12, P.267–288.

LIN D. (1998). An Information-Theoretic Definition of Similarity. Proc.of the International Conference on Machine Learning, USA, .

RESNIK P. (1995). Using information content to evaluate semantic similarity in a taxonomy. Proc. of the14th international joint conference on Artifcial intelligence, V. 1, P.448–453.

YUHUA L. & BANDAR Z.A. & MCLEAN D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on knowledge and data engineering, 15, 4, P.871–882.

SANCHEZ D. & BATET M. & ISERN D. et al. (2012). Ontology-based semantic similarity: A new feature-based approach. Expert Systems with Applications 39, 9, P.7718 – 7728. Available from: https://doi.org/10.1016/j.eswa.2012.01.082. [Accessed 4/07/2022].

RADA R. & MILI H. & BICKNELL E. et al. (1989). Development and application of a metric on semantic nets. IEEE transactions on systems, man, and cybernetics 19, 1, , P.17–30.

WU Z. & PALMER M.. (1994). Verbs semantics and lexical selection. Proc. of the 32nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, P.133–138.

LI Y. & BANDAR Z. A. & MCLEAN D. An approach for measuring semantic similarity between words using multiple information sources.

IEEE Transactions on knowledge and data engineering 15, 4, 2003, P.871–882.

BANERJEE S. & PEDERSEN T. (2003). Extended gloss overlaps as a measure of semantic relatedness. IJCAI, Vol. 3, P.805–810.

JANG Y. & ZHANG X. & TANG Y. et al. (2015). Feature-based approaches to semantic similarity assessment of concepts using Wikipedia. Information Processing & Management 51, 3, , P.215–234.

ZHU G., IGLESIAS C. A. (2017). Computing Semantic Similarity of Concepts in Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering 29, P.72–85. Available from: https://doi.org/10.1109/TKDE.2016.2610428.

JIANG J., CONRATH D. W. (1997). Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Proc.of the 10th Research on Computational Linguistics International Conference , P.19–33.

GAO J.-B. & ZHANG B.-W. & CHEN X.-H. (2015). A WordNet-based semantic similarity measurement combining edge-counting and information content theory. Engineering Applications of Artifcial Intelligence 39, P.80-88. Available from: https://doi.org/10.1016/j. engappai.2014.11.009. [Accessed 12/07/2022].

ROGUSHINA Y.V. (2020). Quantitative evaluations of semantic similarity between the domain concepts as a means of modeling user interests. Applied systems and technologies in the information society, Kyiv National University. University named after Taras Shevchenko, P.183-187.

ROGUSHINA Y.V. & GLADUN A.Y. (2021). Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization. Проблеми програмування, № 2. С.4-15. Available from: http://dspace.nbuv.gov.ua/ handle/123456789/180661. DOI: https://doi.org/10.15407/pp2021.02.003. [Accessed 4/07/2022].

ROGUSHINA J., GLADUN A. (2021). Task Thesaurus as a Tool for Modeling of User Information Needs, Chapter 7 in New Perspectives on Enterprise Decision-Making Applying Artificial Intelligence Techniques, Springer Verlag . 358 p. DOI:10.1007/978-3-030-71115-3_17.

SAATY T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), P.9-26.

ROGUSHINA J. & GLADUN A. (2020). Semantic processing of metadata for Big Data: Standards, ontologies and typical information objects. CEUR Vol-2859, ITS 2020, Information Technologies and Security, 2020, P.114-128. Available from: http://ceur-ws.org/Vol- 2859/paper10.pdf. [Accessed 25/07/2022].




DOI: https://doi.org/10.15407/pp2022.03-04.249

Refbacks

  • There are currently no refbacks.