Volume 8, Issue 3, June 2019, Page: 58-64
Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making
Nadine Mueller, Baden-Wuerttemberg Cooperative State University, Loerrach, Germany
Klemens Schnattinger, Baden-Wuerttemberg Cooperative State University, Loerrach, Germany
Received: May 15, 2019;       Accepted: Jun. 10, 2019;       Published: Jul. 30, 2019
DOI: 10.11648/j.ijiis.20190803.11      View  36      Downloads  9
Abstract
A well-known family of logics for managing structured knowledge is Description logics (DLs). They form the basis for a wide variety of ontology languages. Experience with the use of DLs in applications has, however, shown that their capabilities are insufficient for some domains. In particular, the decision-making process requires the assessment of two, possibly contradictory, influences on decision factors. First, there are items belonging to certain classes or fulfillling certain roles within complex logical constructs, but these memberships are to some extent vague. Secondly, individual preferences may change depending on the person who controls the decision-making process. Therefore, the challenge in building a decision making framework is to appropriately account for these variable influences by depicting and incorporating both aspects. This paper shows how these influences can be best modeled using a combination of fuzzy description logic and weighted description logic. Fuzzy logic is used to represent vagueness and ambiguity in ontologies, weighted description logic expresses individual preferences. In addition, the paper shows how to engineer an appropriate architecture for the suggested model.
Keywords
Ontology Learning, Weighted Description Logic, Fuzzy Logic, Decision Making
To cite this article
Nadine Mueller, Klemens Schnattinger, Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making, International Journal of Intelligent Information Systems. Vol. 8, No. 3, 2019, pp. 58-64. doi: 10.11648/j.ijiis.20190803.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Jung, J., Lee, H., Choi, K.: Contextualized Recommendation Based on Reality Mining from Mobile Subscribers. Cybernetics and Systems. 40 (2), 160-175 (2009).
[2]
Gigerenzer, G., Gaissmaier, W.: Heuristic Decision Making. Annual Review of Psychology 62, 451-482 (2011).
[3]
Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge University Press, First published in 1976 by John Wiley & Sons, Inc. (1993).
[4]
Schnattinger, K., Hahn, U.: Quality-Based Learning. ECAI'98: Proc. 13th Biennial European Conference on Artificial Intelligence, Brighton, UK, 160-164 (1998).
[5]
Lafage, C., Lang, J.: Logical Representation of Preferences for Group Decision Making. KR'00: Proc. 7th Conference on Principles of Knowledge Representation and Reasoning, 457-468 (2000).
[6]
Acar, E., Fink, M., Meilicke, C., Thome, C., Stuckenschmidt, H.: Multi-attribute Decision Making with Weighted Description Logics. IFCoLog: Journal of Logics and its Applications 4, 1973-1995 (2017).
[7]
Straccia, U.: Reasoning within Fuzzy Description Logics. Journal of Artificial Intelligence Research (14), 137-166 (2001).
[8]
Schnattinger, K., Mueller, N., Walterscheid, H.: Consensus Mining - A Guided Group Decision Process for the German Coalition Negotiations. FLAIRS'18: Proc. 31st Florida Artificial Intelligence Research Symposium, Melbourne, USA, 205-208 (2018).
[9]
Schnattinger, K., Walterscheid, H.: Opinion Mining Meets Decision Making: Towards Opinion Engineering. IC3K'17: Proc. 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 334-341 (2017).
[10]
Sun, S., Luo, C., Chen, J.: A Review of Natural Language Processing Techniques for Opinion Mining Systems. Information Fusion 36, 10-25 (2017).
[11]
Baader, F., McGuinness, D., Narci, D., Patel-Schneider, P.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003).
[12]
Hahn, U., Schnattinger, K.: Towards Text Knowledge Engineering. AAAI'98: Proc. 15th National Conference on Artificial Intelligence, 524-531 (1998).
[13]
Herrera-Viedma, E., Alonso, S., Chiclana, F., Herrera, F.: A Consensus Model for Group Decision Making Incomplete Fuzzy Preference Relations. IEEE Transactions on Fuzzy Sytems 15 (5), 863-877 (2007).
[14]
Yager, R. R., Filev, D. P.: Operations for Granular Computing: Mixing Words and Numbers. IEEE International Conference on Fuzzy Systems 2 (1), 123-128 (1998).
[15]
Mueller, N., Schnattinger, K., Walterscheid, H.: Combining Weighted Description Logic with Fuzzy Logic. IPMU'18: Proc. 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 124-136 (2018).
[16]
Horrocks, I., Patel-Schneider, P. F., McGuinness, D. L., Welty, C. A.: OWL: a Description Logic Based Ontology Language for the Semantic Web. In: Description Logic Handbook, 458-486. Cambridge University Press, Cambridge (2007).
[17]
Horrocks, I., Sattler, U., Kutz, O.: The even more irresistible SROIQ. KR'06: Proc. 10th Conference on Principles of Knowledge Representation and Reasoning, 57-67 (2006).
[18]
Motik, B., Patel-Schneider, P. F., Cuenca Grau, B.: OWL 2 Web Ontology Language: Direct Semantics (Second Edition). (Accessed April 2018) Available at: http://www.w3.org/TR/2012/REC-owl2-direct-semantics-20121211/.
[19]
Fishburn, P.: Utility Theory for Decision Making. R. E. Krieger Publications & Co, Huntington, N. Y (1979).
[20]
Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued. Annals of Mathematics and Artificial Intelligence 32 (1-4), 35-66 (2001).
[21]
Zadeh, L. A.: A Computational Approach to Fuzzy Quantifiers in Natural Languages. Computers & Mathematics with Applications 9 (1), 149-184 (1983).
[22]
Hájek, P.: Metamathematics of Fuzzy Logic. Springer, Dordrecht; Netherlans (1998).
[23]
Straccia, U.: All About Fuzzy Description Logics and Applications. In Faber, W., Paschke, A., eds.: Reasoning Web. Web Logic Rules, Cham, 1-31 (2015).
[24]
Stoilos, G., et. al.: Reasoning with Very Expressive Fuzzy Description Logics. Journal of Artificial Intelligence Research (30), 273–320 (2007).
[25]
Yager, R., Basson, D.: Decision Making with Fuzzy Sets. Fuzzy Sets and Systems, 87-95 (1978).
[26]
Petrucci G., Ghidini C., Rospocher M.: Ontology Learning in the Deep. In: Blomqvist E., Ciancarini P., Poggi F., Vitali F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science, Springer, 480-495 (2016).
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