Abstract: Aiming at the problem that it was difficult to accurately represent and process  uncertain knowledge in a single way due to its ambiguity,  randomness or  incompleteness, we proposed an uncertainty knowledge representation algorithm based on resource description framework (RDF) and probabilistic reasoning. Firstly,  RDF graph was used to describe uncertain knowledge data samples, and uncertain knowledge meta statements and their hierarchical relationships were constructed to obtain uncertain knowledge RDF graph patterns and standard statement patterns. Secondly, fuzzy Petri nets were used to represent uncertain knowledge and define fuzzy Petri net octets.  The probabilistic soft logic reasoning was used to construct fuzzy inference rules and constrain the logical inference rules. Finally, through specific operator inference, we output uncertain knowledge representation results when the library credibility value was stable. The experimental results show that the semantic richness values of the standard sentences constructed by proposed method are all higher than 0.8. When the number of logical rules increases to 220, 
there are only 3 occurrences of logical contradictions, with a probability of 1.36%. The certainty of different uncertain knowledge representations is all above 0.9, indicating that the algorithm has high accuracy in representing uncertain knowledge and can effectively capture and describe the logical relationships of variables within the knowledge.

Key words:  , resource description framework graph, probabilistic soft reasoning, uncertainty, knowledge representation, fuzzy Petri net