GaussianPath:A Bayesian Multi-Hop Reasoning Framework for Knowledge Graph Reasoning

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) has attracted wide attention due to its desirable interpretability for downstream tasks, such as question answer and knowledge graph completion. Multi-Hop reasoning is a typical sequential decision problem, which can be formulated as a Markov decision process (MDP). Subsequently, some reinforcement learning (RL) based approaches are proposed and proven effective to train an agent for reasoning paths sequentially until reaching the target answer. However, these approaches assume that an entity/relation representation follows a one-point distribution. In fact, different entities and relations may contain different certainties. On the other hand, since REINFORCE used for updating the policy in these approaches is a biased policy gradients method, the agent is prone to be stuck in high reward paths rather than broad reasoning paths, which leads to premature and suboptimal exploitation. In this paper, we consider a Bayesian reinforcement learning paradigm to harness uncertainty into multi-hop reasoning. By incorporating uncertainty into the representation layer, the agent trained by RL has uncertainty in a region of the state space then it should be more efficient in exploring unknown or less known part of the KG. In our approach, we build a Bayesian Q-learning architecture as a state-action value function for estimating the expected long-term reward. As initialized by Gaussian prior or pre-trained prior distribution, the representation layer drives uncertainty that allows regularizing the training. We conducted extensive experiments on multiple KGs. Experimental results show a superior performance than other baselines, especially significant improvements on the automated extracted KG

Reinforcement Learning based meta-path discovery in large-scale Heterogeneous Information Networks

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, most HIN analysis still relies on handcrafting meta-paths, which requires rich domain knowledge that is extremely difficult to obtain in complex, large-scale, and schema-rich HINs. In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. To capture different semantic information between objects, we propose a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity. To improve the efficiency, moreover, we develop a type context representation embedded approach to scale the RL framework to handle million-scale HINs. As multi-hop reasoning generates rich meta-paths with various length, we further perform a meta-path induction step to summarize the important meta-paths using Lowest Common Ancestor principle. Experimental results on two large-scale HINs, Yago and NELL, validate our approach and demonstrate that our algorithm not only achieves superior performance in the link prediction task, but also identifies useful meta-paths that would have been ignored by human experts

Tool life prediction under multi-cycle loading conditions: A feasibility study

In the present research, the friction and wear behaviour of a hard coating were studied by using ball-on-disc tests to simulate the wear process of the coated tools for sheet metal forming process. The evolution of the friction coefficient followed a typical dual-plateau pattern, i.e. at the initial stage of sliding, the friction coefficient was relatively low, followed by a sharp increase due to the breakdown of the coatings after a certain number of cyclic dynamic loadings. This phenomenon was caused by the interactive response between the friction and wear from a coating tribo-system, which has not been addressed so far by metal forming researchers, and constant friction coefficient values are normally used in the FE simulations to represent the complex tribological nature at the contact interfaces. Meanwhile, most of the current FE simulations are single cycle, whereas most sheet metal forming operations are conducted as multi-cycle. Therefore, a novel friction/wear interactive friction model was developed to, simultaneously, characterise the evolutions of friction coefficient and the remaining thickness of the coating layer, to enable the wear life of coated tooling to be predicted. The friction model was then implemented into the FE simulation of a sheet metal forming process for feasibility study

Tool life prediction under multi-cycle loading conditions: A feasibility study

In the present research, the friction and wear behaviour of a hard coating were studied by using ball-on-disc tests to simulate the wear process of the coated tools for sheet metal forming process. The evolution of the friction coefficient followed a typical dual-plateau pattern, i.e. at the initial stage of sliding, the friction coefficient was relatively low, followed by a sharp increase due to the breakdown of the coatings after a certain number of cyclic dynamic loadings. This phenomenon was caused by the interactive response between the friction and wear from a coating tribo-system, which has not been addressed so far by metal forming researchers, and constant friction coefficient values are normally used in the FE simulations to represent the complex tribological nature at the contact interfaces. Meanwhile, most of the current FE simulations are single cycle, whereas most sheet metal forming operations are conducted as multi-cycle. Therefore, a novel friction/wear interactive friction model was developed to, simultaneously, characterise the evolutions of friction coefficient and the remaining thickness of the coating layer, to enable the wear life of coated tooling to be predicted. The friction model was then implemented into the FE simulation of a sheet metal forming process for feasibility study

Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis

AbstractBackground Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.Method Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.Results Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.Conclusions Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis