Open Access

ARTICLE

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

Dun Li¹, Hong Wu¹, Jinzhu Gao², Zhuoyun Liu¹, Lun Li¹, Zhiyun Zheng^1,*

1 Zhengzhou University, 100 Science Avenue, Zhengzhou, 450001, China.
2 University of the Pacific, 3601 Pacific Avenue, Stockton, 95211, USA.

* Corresponding Author: Zhiyun Zheng. Email: .

Computers, Materials & Continua 2019, 61(1), 301-321. https://doi.org/10.32604/cmc.2019.05953

Abstract

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly, the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm. The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL. After that, the reasoning process, including the SSFBN structure algorithm, data fuzzification, reasoning of fuzzy rules, and fuzzy belief propagation, is scheduled. Finally, compared with the classical algorithm from the aspect of accuracy and time complexity, our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity, which proves the feasibility and validity of our solution to represent and reason uncertain information.

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Citations