Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Purpose:Given the information overload of scientific literature,there is an increasing need for computable biomedical knowledge buried in free text.This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements.Design/methodology/approach:Taking cardiovascular research publications in China as a sample,we extracted subject-predicate-object triples(SPO triples)as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context.We introduced information entropy(IE)as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs(SO pairs)levels.Findings:The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples.After evaluating the uncertainty of biomedical knowledge with IE,we identified the Top 10 SO pairs with highest IE,which implied the epistemic status pluralism.Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research.Research limitations:The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words.The number of sentences surrounding a given triple may also influence the value of IE.Practical implications:Our approach identified major uncertain knowledge areas such as diagnostic biomarkers,genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China.These areas are suggested to be prioritized;new hypotheses need to be verified,while disputes,conflicts,and contradictions need to be settled.Originality/value:We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.

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.

云计算中的群体智能与不确定性知识表示(英文)

The lasting evolution of computing environment, software engineering and interaction methods leads to cloud computing. Cloud computing changes the configuration mode of resources on the Internet and all kinds of resources are virtualized and provided as services. Mass participation and online interaction with social annotations become usual in human daily life. People who own similar interests on the Internet may cluster naturally into scalable and boundless communities and collective intelligence will emerge. Human is taken as an intelligent computing factor, and uncertainty becomes a basic property in cloud computing. Virtualization, soft computing and granular computing will become essential features of cloud computing. Compared with the engineering technological problems of IaaS (Infrastructure as a service), PaaS (Platform as a Service) and SaaS (Software as a Service), collective intelligence and uncertain knowledge representation will be more important frontiers in cloud computing for researchers within the community of intelligence science.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.