Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning

In this paper,to deal with the heterogeneity in federated learning(FL)systems,a knowledge distillation(KD)driven training framework for FL is proposed,where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.To overcome the challenge of train the big teacher model in resource limited user devices,the digital twin(DT)is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources.Then,during model distillation,each user can update the parameters of its model at either the physical entity or the digital agent.The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming(MIP)problem.To solve the problem,Q-learning and optimization are jointly used,where Q-learning selects models for users and determines whether to train locally or on the server,and optimization is used to allocate resources for users based on the output of Q-learning.Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

A Semantic-based Clustering Method to Build Domain Ontology from Multiple Heterogeneous Knowledge Sources

In order to improve the efficiency of ontology construction from heterogeneous knowledge sources, a semantic-based approach is presented. The ontology will be constructed with the application of cluster technique in an incremental way. Firstly, terms will be extracted from knowledge sources and congregate a term set after pretreat-ment. Then the concept set will be built via semantic-based clustering according to semanteme of terms provided by WordNet. Next, a concept tree is constructed in terms of mapping rules between semant^me relationships and concept relationships. The semi-automatic approach can avoid non-consistence due to knowledge engineers having different understanding of the same concept and the obtained ontology is easily to be expanded.

The Relation between Knowledge Heterogeneity and Knowledge Innovation Performance of R &D Team

Using a sample of 252 R & D teams in Guangzhou, Foshan, Shenzhen, the researcher empirically examines the relationship between knowledge heterogeneity and knowledge innovation performance, the mediating role of knowledge share. Results indicate that knowledge heterogeneity is positively related to knowledge share, the same with knowledge share and knowledge innovation performance. This paper analyzes the results comprehensively and makes recommendations from multiple perspectives including building the knowledge heterogeneous steams, advocating the collaborative spirit, building a knowledge shared platform, improving the organizational structure, and grooming the communication.

Influence of Knowledge Heterogeneity on Innovation Performance in Innovation Teams——Based on the Mediating Role of Knowledge Sharing

To break through the restrictions of traditional organizational forms,systems,and mechanisms and quickly respond to the innovative development requirements of CASC,the innovation team has gradually become a crucial organizational form within CASC.One of the biggest differences between the innovation team and traditional orga-nizational structure lies in knowledge heterogeneity.Existing studies present different conclusions on the relationship between knowledge heterogeneity and innovation performance,which should be analyzed according to specific situ-ations.Therefore,this paper takes the innovation team of CASC as the research object to conduct an empirical study on 186 team members,propose conceptual models and hypotheses,and study the relationship among knowledge heterogeneity,knowledge sharing,and innovation performance.The research results indicate that the two dimensions of knowledge heterogeneity—explicit knowledge heterogeneity and implicit knowledge heterogeneity—are beneficial to innovation performance when they are to a great extent.Knowledge sharing plays a partially mediating role between knowledge heterogeneity and collaborative innovation performance.It reveals the influence of knowledge heterogene-ity on innovation performance in the innovation team of CASC,aiming to provide a certain reference for the establish-ment and development of CASC’s innovation team.

Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge

Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience replay.Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.

Reliable knowledge graph fact prediction via reinforcement learning

Knowledge graph(KG)fact prediction aims to complete a KG by determining the truthfulness of predicted triples.Reinforcement learning(RL)-based approaches have been widely used for fact prediction.However,the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths,thereby resulting in unreliable decisions on prediction triples.Hence,we propose a new RL-based approach named EvoPath in this study.EvoPath features a new reward mechanism based on entity heterogeneity,facilitating an agent to obtain effective reasoning paths during random walks.EvoPath also incorporates a new postwalking mechanism to leverage easily overlooked but valuable reasoning paths during RL.Both mechanisms provide sufficient reasoning paths to facilitate the reliable calculations of rule confidences,enabling EvoPath to make precise judgments about the truthfulness of prediction triples.Experiments demonstrate that EvoPath can achieve more accurate fact predictions than existing approaches.

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

Highly Resilient Key Distribution Strategy for Multi-level Heterogeneous Sensor Networks by Using Deployment Knowledge

The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.

Integrating Manifold Knowledge for Global Entity Linking with Heterogeneous Graphs

Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods attempt to model the topical coherence among all linked entities,most of them failed in exploiting the correlations among manifold knowledge helpful for linking,such as the semantics of mentions and their candidates,the neighborhood information of candidate entities in KB and the fine-grained type information of entities.As we will show in the paper,interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document.In this paper,we present a novel HEterogeneous Graph-based Entity Linker(HEGEL)for global entity linking,which builds an informative heterogeneous graph for every document to collect various linking clues.Then HEGEL utilizes a novel heterogeneous graph neural network(HGNN)to integrate the different types of manifold information and model the interactions among them.Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods.

Entity set expansion in knowledge graph:a heterogeneous information network perspective

Entity set expansion(ESE)aims to expand an entity seed set to obtain more entities which have common properties.ESE is important for many applications such as dictionary con-struction and query suggestion.Traditional ESE methods relied heavily on the text and Web information of entities.Recently,some ESE methods employed knowledge graphs(KGs)to extend entities.However,they failed to effectively and fficiently utilize the rich semantics contained in a KG and ignored the text information of entities in Wikipedia.In this paper,we model a KG as a heterogeneous information network(HIN)containing multiple types of objects and relations.Fine-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate entities.Then we rank the entities according to the meta path based structural similarity.Furthermore,to utilize the text description of entities in Wikipedia,we propose an extended model CoMeSE++which combines both structural information revealed by a KG and text information in Wikipedia for ESE.Extensive experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.

基于知识标注平台的水利枢纽工程知识图谱构建及应用

大量水利异构数据的产生,为领域知识图谱的构建及应用提供了场景,但也导致了水利知识图谱构建过程的差异。针对现有水利知识图谱构建流程复杂的问题,提出了一套有效的基于知识标注平台的水利知识图谱构建流程。以小浪底水利枢纽工程知识的智能应用为例,使用该枢纽的工程数据,应用提出的流程在水利领域构建水利枢纽工程知识图谱(Water Conservancy Hub Project Knowledge Graph,WCHP-KG)。首先以小浪底水利枢纽工程为中心,依据行业术语标准和现有词汇表,制定了概念分类和关系描述体系,形成了WCHP-KG的模式层。通过BiLSTM-CRF和序列标注模型,在水利专家的指导下,使用知识标注平台对非结构化文本进行了半自动标注和人工校对,实现了知识融合,进而构建了WCHP-KG的数据层。结果表明WCHP-KG涵盖了43种水利实体以及110种实体关系。经过实践验证,构建的WCHP-KG为小浪底水利枢纽工程的相关应用提供了有力的结构化知识基础,为工程决策和管理提供了可靠的参考依据,进而证明了所提构建流程的有效性。未来将进一步扩展WCHP-KG和完善水利知识图谱的构建流程,以适应更多的应用场景和领域需求。

融合遗忘机制的多模态知识追踪模型

知识追踪是构建自适应教育系统的核心和关键,常被用以捕获学生的知识状态、预测学生的未来表现。以往的知识追踪模型仅根据结构信息对问题、技能进行建模,无法利用问题、技能的多模态信息构造其相互依赖关系。同时,关于学生的记忆水平仅以时间做量化,未考虑不同模态对记忆水平的影响。因此,提出了融合遗忘机制的多模态知识追踪模型。首先,对问题、技能节点,以图文匹配作为训练任务优化单模态嵌入,并通过计算多模态融合后节点间的相似度,获得问题和技能的关联权重从而计算生成问题节点的嵌入。其次,通过长短期记忆网络获取带有遗忘因素的学生知识状态,并将其融入学生的答题记录中生成学生节点的嵌入。最后,根据学生的答题次数和不同模态的有效记忆率计算学生和问题间的关联强度,通过图注意力网络进行信息传播,预测学生对不同问题的答题情况。在两个真实课堂自采数据集上进行了对比实验和消融实验,结果表明所提方法比其他基于图的知识追踪模型具有更好的预测精度,且针对多模态和遗忘机制的设计能有效提升原始模型的预测效果。同时,通过对一个具体案例的可视化分析,进一步说明了所提方法的实际应用效果。

知识共创视角下协同要素异质性、知识场活性对社区创新绩效的影响

以资源基础理论和团队异质性理论为依托,从知识共创视角提炼出社区知识协同中多维要素异质性的计量方法和共创情境的表征变量,利用Python程序爬取魅族社区数据,考察不同要素对创新绩效的差异化影响,以及知识场活性在其中发挥的中介传递作用。研究结果显示:知识共创过程中用户异质性、内容异质性和表达形式异质性对社区知识场活性具有正向影响,情感异质性对知识场活性具有倒U型影响;知识场活性对创新绩效具有正向影响,且在用户异质性、内容异质性、表达形式异质性与创新绩效关系中发挥部分中介作用。

行业协会嵌入对企业数字化转型的影响研究

选取2007~2021年A股上市制造企业为研究样本,考察行业协会嵌入对企业破解数字化转型困局所发挥的作用。研究发现:行业协会嵌入通过资源赋能与合法性感知机制促进了企业数字化转型;高管团队数字知识与职能背景异质性通过资源赋能路径、行业数字化水平与地区市场化程度通过合法性感知路径共同强化了上述关系。进一步研究发现,行业协会嵌入通过降低融资约束、提高吸收能力与降低经济政策不确定性感知3条路径推动企业数字化转型;且这一影响效应在民营企业、无政治关联企业和小规模企业中更为明显。

扬长避短:优势使用视角下知识异质性对越轨创新的影响机制研究

有效利用员工优势资源提高竞争力是企业关注的重点,但目前对于如何有效化解组织内非协同性导致的认知冲突尚未给予关注。鉴于此,关注知识异质性的双重属性,结合个体优势使用理论,通过问卷调查的形式收集到381份问卷数据,实证分析知识异质性对越轨创新影响过程中员工优势使用的中介作用以及领导权变激励的边界条件。结果表明:(1)知识异质性能够对越轨创新产生积极影响;(2)员工优势使用在知识异质性与越轨创新之间具有部分中介作用,即知识异质性可以促进员工的优势使用,进而对越轨创新产生积极影响;(3)领导权变激励会强化员工优势使用与越轨创新之间的正向关系,同时正向调节知识异质性通过员工优势使用影响越轨创新的间接效应。据此,提出企业应重视和开发知识异质性员工的价值、重视员工优势的开发,并采取及时有效的领导激励,以充分利用员工差异化知识储备、将员工自身优势转化为创新性成果,使企业能够长期保持核心竞争优势。

基于中文电子病历知识图谱的实体对齐研究

医疗知识图谱中知识重叠和互补的现象普遍存在,利用实体对齐进行医疗知识图谱融合成为迫切需要。然而据作者调研,目前医疗领域中的实体对齐尚没有一个完整的处理方案。因此该文提出了一个规范的基于中文电子病历的医疗知识图谱实体对齐流程,为医疗领域的实体对齐提供了一种可行的方案。同时针对基于中文电子病历医疗知识图谱之间结构异构性的特点,该文设计了一个双视角并行图神经网络(DuPNet)模型用于解决医疗领域实体对齐,并取得较好的效果。

基于双粒度语义特征与异质性网络的知识共创价值识别

[研究目的]旨在优化虚拟社区中知识共创价值的识别方法,缓解因信息过载和关联复杂性等导致的高价值性知识资源识别效果不佳的问题。[研究方法]从知识共创的动态协同过程入手,构建集成双粒度语义与异质性网络的知识共创价值识别模型(DGSHAN)。首先利用BERT、Sentence-BERT并行获取词、句双粒度知识单元的语义信息,继而引入CNN、BiLSTM差异化提炼协同知识的局部内核特征与动态时序特征;同时采用HAN处理异质性关联网络,挖掘用户交互下多类型实体与拓扑结构中的关联规律,最后融合知识资源组合和用户行为互动双链路特征,实现知识共创价值的有效识别。[研究结论]经魅族社区Flyme数据验证,该模型的识别准确度、宏F1、加权F1分别为82.16%、73.56%、81.39%,相较于其他基线模型,各评估指标都有显著提高,可以有效提升知识共创价值的识别效果。

融合异构图神经网络的时间卷积知识追踪方法

知识追踪任务旨在通过建模学生历史学习序列追踪学生认知水平,进而预测学生未来的答题表现.该文提出一个融合异构图神经网络的时间卷积知识追踪模型(Temporal Convolutional Knowledge Tracing Model with Heterogeneous Graph Neural Network,HG-TCKT),将知识追踪任务重述为基于异构图神经网络的时序边分类问题.具体来说,首先将学习记录构建成包含3种节点类型(学生,习题和技能),2种边类型(学生-习题和习题-技能)的异构图数据,异构图描述了学生交互记录中实体类型之间的丰富关系,使用异构图神经网络缓解交互稀疏的问题,引入异构互注意力机制捕捉不同类型节点间的交互关系,提取不同类型节点的高阶特征.将学生节点和习题节点表征拼接,构造边(学生-习题)的表征.最后,使用时间卷积网络捕捉学生历史交互序列的时序依赖关系从而进行预测.在2个真实教育数据集进行实验证明,HG-TCKT相比当前主流知识追踪方法有更好的预测效果.

模块知识融合反应工程大作业设计的思考

化学反应工程是高校化学工程专业的重要核心课之一,致力于研究化学动力学并根据反应特点设计合适的反应器。课程教学团队根据多年教学经验,将课程内容分为理想反应器、非理想流动、非均相催化和工业反应器四个模块,并针对非理想流动与非均相催化模块的知识关联较弱而导致学生无法综合运用所学知识解决复杂反应工程问题的现象,设计了基于模块知识融合的课程大作业。本文阐述了课程大作业对加强模块间知识关联的重要作用,梳理了课程大作业的设计和实施要点,并以催化剂氧空位测量的课程大作业为例,介绍如何通过非理想流动和非均相催化模块知识的融合提升学生解决复杂工程问题的能力,最后总结了课程大作业实施过程中的共性问题并提出了改进建议。