Isomorphic Transformations of Uncertaintiesfor Incorporating EMYCIN-Style andPROSPECTOR-Style Systems intoa Distributed Expert System

In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can usethe Internet to couple them together, their performance will be improved throughcooperation. This is because the problem-solving ability of expert systems is greatlyimproved by the way of cooperation among different expert systems in a distributedexpert system. Cooperation between different expert systems with these two het-erogeneous uncertain reasoning models is essentially based on the transformations ofuncertainties of propositions between these two models. In this paper, we discoveredthe exactly isomorphic transformations uncertainties between uncertain reasoningmodels, as used by EMYCIN and PROSPECTOR.

Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.