Use of ontological networks in decision support systems under ambiguity

: pp. 8 - 15
Lviv Polytechnic National University, Information Systems and Network
Lviv Polytechnic National University, Information Systems and Networks Department

The increasing amount of information that needs to be taken into account in decision making determines the relevance of building intelligent decision support systems. The prerequisite for making the right decision is to build a correct conceptual model of the problem situation, which takes into account all the factors relevant to this situation. The conceptualization of the problem situation is presented by the ontology of that situation. When forming the ontology of a situation, it is advisable to use knowledge from existing ontologies. This raises the problem of ambiguity in the selection of elements of existing ontologies that most accurately fit the situation.

The purpose of the work is to investigate the usage of ontology networks for the construction of ontologies for problematic situations in the conditions of choice ambiguity, that is, when it is necessary to select the most accurate ontological source taking in consideration the context.

Formal definitions of the ontology of the problem situation, the correspondence between the elements of the ontology, the network of ontologies and the rules of conformity are given. Matching rules are defined as mappings between subsets of concepts, relationships, and interpretation functions of two ontologies. The paper presents a conceptual model of ontology formation of a problematic situation based on several source ontologies. The structure of decision support system on the basis of ontological networks was developed and the process of decision support in case of using ontology networks was defined. A central element of such a system is the knowledge base, which contains situation models and links to external ontologies from the network for each such model. The basis of these references is a set of articulation rules that determine which ontologies should be used to acquire knowledge from and how to transform them before writing them into an ontology of a situation. When a problematic situation occurs, the ontology of the situation is formed dynamically, taking into account the existing context of the situation. It also provides an opportunity to use current knowledge in related ontologies.

Proposed approaches for building decision support systems using ontology networks provide the ability to dynamically select concepts and relationships that are relevant to the context of the situation. The results of the work should be used to develop decision support systems that require data from different subject areas in conditions of ambiguity.

Aditya Das. (2013). Artificial intelligence and decision support systems. Retrieved February 02, 2020, from

Herre, H. (2010). General Formal Ontology (GFO): A foundational ontology for conceptual modelling. In Theory and applications of ontology: computer applications (pp. 297-345). Springer, Dordrecht.

Raz et al. (2006). Fast and Efficient Context-Aware Services. John Wiley & Sons

Prasenjit, M., & Gio, W. (2003). An Ontology-Composition Algebra.

Gavrilova, T. А. (2001). Structure description. Intelligent systems knowledge base, (pp. 56).

Tarapata, Z. (2007). Multicriteria weighted graphs similarity and its application for decision situation pattern matching problem. In Proceedings of the 13th IEEE/IFAC International Conference on Methods and Models in Automation and Robotics (pp. 1149-1155).

Miah, S.J., Gammack, J. & Kerr, D., (2007). Ontology development for context-sensitive decision support. In Semantics, Knowledge and Grid, Third International Conference on (pp. 475-478). IEEE.

Rahim, N. R., Nordin, S., & Dom, R. M. (2019). A Clinical Decision Support System based on Ontology and Causal Reasoning Models. Jurnal Intelek, 14(2), 187-197.

Euzenat, J. (2008). Algebras of ontology alignment relations.

Euzenat, J., David, J., Locoro, A., & Inants, A. (2015). Context-based ontology matching and data interlinking.

Sánchez, D., Batet, M., Isern, D., & Valls, A. (2012). Ontology-based semantic similarity: A new featurebased approach. Expert Syst. Appl., 39, 7718-7728.

Lytvyn, V., Vysotska, V., Peleshchak, I., Basyuk, T., Kovalchuk, V., Kubinska, S., ... & Salo, T. (2019, September). Identifying Textual Content Based on Thematic Analysis of Similar Texts in Big Data. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 2, pp. 84-91). IEEE.

Vysotska, V., Lytvyn, V., Kovalchuk, V., Kubinska, S., Dilai, M., Chyrun, L., ... & Brodyak, O. (2019, September). Method of Similar Textual Content Selection Based on Thematic Information Retrieval. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp. 1-6). IEEE.

Hermann, H. (2006). Knowledge Representation and the Semantics of Natural Language.

Alter, S. L. (1980). Decision support systems : current practice and continuing challenges.

Scott, M. S. (1971). Management Decision Systems: Computer-based Support for Decision Making.

Marakas, G. M. (1999). Decision support systems in the twenty-first century.