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.
| Published in | International Journal of Intelligent Information Systems (Volume 8, Issue 3) |
| DOI | 10.11648/j.ijiis.20190803.11 |
| Page(s) | 58-64 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Ontology Learning, Weighted Description Logic, Fuzzy Logic, Decision Making
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APA Style
Nadine Mueller, Klemens Schnattinger. (2019). Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making. International Journal of Intelligent Information Systems, 8(3), 58-64. https://doi.org/10.11648/j.ijiis.20190803.11
ACS Style
Nadine Mueller; Klemens Schnattinger. Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making. Int. J. Intell. Inf. Syst. 2019, 8(3), 58-64. doi: 10.11648/j.ijiis.20190803.11
AMA Style
Nadine Mueller, Klemens Schnattinger. Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making. Int J Intell Inf Syst. 2019;8(3):58-64. doi: 10.11648/j.ijiis.20190803.11
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Download@article{10.11648/j.ijiis.20190803.11,
author = {Nadine Mueller and Klemens Schnattinger},
title = {Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making},
journal = {International Journal of Intelligent Information Systems},
volume = {8},
number = {3},
pages = {58-64},
doi = {10.11648/j.ijiis.20190803.11},
url = {https://doi.org/10.11648/j.ijiis.20190803.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20190803.11},
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.},
year = {2019}
}
TY - JOUR T1 - Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making AU - Nadine Mueller AU - Klemens Schnattinger Y1 - 2019/07/30 PY - 2019 N1 - https://doi.org/10.11648/j.ijiis.20190803.11 DO - 10.11648/j.ijiis.20190803.11 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 58 EP - 64 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20190803.11 AB - 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. VL - 8 IS - 3 ER -
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