Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.

Ultra-lightweight CNN design based on neural architecture search and knowledge distillation: A novel method to build the automatic recognition model of space target ISAR images

In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.

A dynamic knowledge base based search engine

Search engines have greatly helped us to find thedesired information from the Intemet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine (DKBSE), which can expand the user's query using the keywords' concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system's searching results and the user's feedback information. The DKBSE expands the user's initial query using the knowledge base, and returns the searched information after the expanded query.

ISI Web of Knowledge跨库检索功能与操作技巧

ISI Web of Knowledge是新一代的学术信息资源整合体系,是Web of science的最新升级平台。 本文主要介绍了这一体系的跨库检索功能及其操作技巧。

Web of Knowledge及其检索功能研究

分析了知网信息量大、链接多、检索功能全等特点,同时也指出了知网操作复杂等缺陷。

Survey of Knowledge Graph Approaches and Applications

With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.

A Solution to Knowledge Management in Networked Collaborative Design

Concerning the demands in networked collaborative innovative design, a knowledge-based collaborative design model is introduced the model of knowledge integration along with its relevant supporting techniques are presented. After illustrating the general knowledge search paradigm, a kind of dynamic user model is proposed to improve knowledge search efficiency. At last a short introduction of the system’s implementation is described.

Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

The meta-heuristic algorithm with local search is an excellent choice for the job-shop scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

初创企业的知识搜索对创业机会识别影响机制--基于企业数字化能力的中介作用

基于知识基础观和动态能力理论,构建知识搜索、企业数字化能力与创业机会识别的理论模型,对251个初创企业创始人样本数据开展信效度检验、共同方法偏差检验、描述统计、相关分析与假设检验,结果显示,知识搜索宽度和深度均正向影响创业机会识别,企业数字化能力促进创业机会识别;数字感知能力、数字协同能力和数字运营能力分别在知识搜索和促进创业机会识别关系中起部分中介作用,其中数字协同能力的中介作用最强。因此初创企业应合理采取知识搜索策略识别创业机会,重视企业数字化能力的培育以及发挥其在知识搜索与创业机会识别间的传导作用。

Seru系统调度优化的知识引导协同进化算法

作为一种新型的生产模式, Seru系统能够兼顾柔性和效率且快速响应市场,已在装配企业得到广泛应用.为了实现实际生产过程生产效率和劳动效率的协同优化,本文研究以最小化最大完工时间和工人总劳动时间为目标的Seru系统多目标调度问题,提出一种知识引导的协同进化算法.首先,将问题分解为Seru构造和Seru调度,构造两个种群分别优化子问题.同时,设计种群规模的调整策略,通过为有潜力的种群分配更多个体来提高协同搜索的效率.进而,通过分析问题的性质,提炼规则性知识用于设计有效的搜索算子和重生成规则,指导精英个体执行知识驱动的增强搜索,从而进一步提升算法的局部开发能力.通过数值仿真和统计性能对比,验证了算法各设计环节的有效性,并取得了显著优于现有最新算法的多目标调度优化性能.

异质性环境规制对企业差异化绿色创新行为的影响——知识搜寻的中介作用

环境规制与绿色创新的关系一直备受争议,这可能与多数文献忽视环境规制(命令型与市场型)与绿色创新(策略性与实质性)的双重异质性有关。基于制度理论与知识管理理论,构建“环境规制—知识搜寻—企业绿色创新”理论框架,综合运用多元线性回归与模糊集定性比较分析(fsQCA)方法,采用285份企业调查问卷验证研究假设。结果显示:命令型环境规制仅能促进策略性绿色创新,市场型环境规制仅能促进实质性绿色创新;知识搜寻深度在命令型环境规制与策略性绿色创新间发挥中介作用,知识搜寻广度在市场型环境规制与实质性绿色创新间发挥中介作用。进一步地,通过fsQCA分析得到5种前因构型,其中,高策略性绿色创新与高实质性绿色创新的组态路径分别有2条和3条。结论可拓展环境规制对企业绿色创新影响的研究维度,并为政府调整环境规制政策、企业应对外部制度压力提供参考。

跨界知识搜寻、吸收能力对制造企业突破式创新的影响研究

基于创新理论、资源基础观理论与组织学习理论,构建跨界知识搜寻、吸收能力、战略柔性与企业突破式创新的理论模型和分析框架,运用SPSS和SmartPLS软件进行实证检验。研究结果表明:技术知识跨界搜寻正向影响企业突破式创新;市场知识跨界搜寻对企业突破式创新没有显著影响;知识跨界搜寻正向影响企业吸收能力;吸收能力正向影响企业突破式创新;吸收能力在跨界知识搜寻与企业突破式创新之间发挥显著中介作用;战略柔性在跨界知识搜寻对企业突破式创新的影响中发挥显著调节作用。

知识搜寻与重构如何促进专精特新中小企业绿色技术创新——战略柔性与激励型环境规制的调节作用

专精特新中小企业的创新实力和发展韧性较强,如何促进其绿色技术创新对于实现“双碳”目标和企业可持续发展具有重要意义。基于组织搜寻与知识管理理论,引入知识重构作为中介变量,战略柔性与激励型环境规制作为调节变量,探究知识搜寻对专精特新中小企业绿色技术创新的内在作用机制。基于272家专精特新中小企业调查数据,通过层次回归、Bootstrap等方法进行分析。结果表明:市场知识搜寻与技术知识搜寻均对专精特新中小企业绿色技术创新具有显著影响;知识重构在市场知识搜寻、技术知识搜寻与专精特新中小企业绿色技术创新之间起部分中介作用;战略柔性正向调节知识搜寻对知识重构的影响;激励型环境规制正向调节知识重构对专精特新中小企业绿色技术创新的影响。

技术多元化能够促进企业绩效提升吗--技术重组模式与科学知识搜索的调节作用

技术多元化是企业获取竞争优势的关键,对于其如何影响企业绩效这一问题,学界存在不同观点。解决跨领域技术融合难题是企业从技术多元化战略中获益的关键,然而现有文献忽略了不同企业技术重组搜索特征差异,对于如何优化技术重组搜索过程以提升技术多元化战略实施效果这一问题尚未提供答案。从技术重组搜索视角出发,探究企业技术重组模式、科学知识搜索对技术多元化与企业绩效关系的调节作用。基于我国高技术产业上市公司面板数据的实证研究发现:技术多元化与企业绩效呈倒U型关系;技术重组创造促使该倒U型关系曲线变得平坦,技术重组再利用促使该倒U型关系曲线变得陡峭;科学知识搜索强度的调节作用不显著,但在企业科学知识搜索强度较高且擅长技术重组创造的情况下,技术多元化与绩效正相关,从而将“技术多元化陷阱”转变为“技术多元化优势”。随着技术多元化程度加深,企业不仅可以通过积累资源,而且能够通过优化技术重组过程,由技术重组再利用模式向技术重组创造模式转变,并通过加强科学知识搜索运用从技术多元化战略中获益。

前瞻性外部知识搜索对突破性绿色技术创新的影响

突破性绿色技术创新是企业高质量发展的重要途径,然而关于前瞻性外部知识搜索影响突破性绿色技术创新的机理仍不明晰。以我国制造企业为研究对象,运用知识基础观和动态能力理论,探讨前瞻性外部知识搜索对企业突破性绿色技术创新的影响,分析绿色动态能力的中介作用和环境规制的调节作用。结果表明:前瞻性外部知识搜索不仅对突破性绿色技术创新具有显著正向影响,而且对绿色环境适应能力、绿色知识吸收能力和绿色资源整合能力具有显著正向影响;绿色环境适应能力、绿色知识吸收能力和绿色资源整合能力对突破性绿色技术创新均具有显著正向影响,并且在前瞻性外部知识搜索与突破性绿色技术创新之间起完全中介作用;环境规制正向调节前瞻性外部知识搜索与绿色环境适应能力、绿色知识吸收能力以及绿色资源整合能力的关系。

不确定冲击下全要素创新、组织韧性与企业绩效:远程知识搜索的调节

不确定冲击下组织韧性日益成为企业抵御风险的一种重要生存能力,如何通过企业创新增强组织韧性以确保企业生存和长期发展成为当下亟待解决的现实问题。基于组织韧性和企业创新理论,对温州362家制造企业问卷调查,探索企业全要素创新、组织韧性与企业绩效的关系,以及远程知识搜索的调节影响。结果显示:企业全要素创新有利于企业绩效,其中技术创新的边际效应大于非技术创新,且二者存在替代效应;技术创新和非技术创新通过增强组织韧性的部分中介效应促进企业绩效;远程知识搜索增强了技术创新对组织韧性的正向影响,而对非技术创新的影响不显著。

数字记忆中的人文色彩:基于中外档案数字记忆项目的调查分析

数字记忆是记忆在数字时代的新形态。档案具有的文化属性、档案工作对全宗完整性的重视、档案服务的社会化趋势以及档案研究对人文议题的观照成为档案数字记忆人文色彩建构中的优势。通过对国内外档案数字记忆项目的调研,发现档案数字记忆项目缺少对记忆资源深层面的意义挖掘,难以实现对档案资源语义的关联和理解以及复合型的高级检索,而社会公众的缺位使档案数字记忆项目与数据知识库无异。为增强档案数字记忆中的人文色彩,以档案叙事理论为指导,以塑造纷众记忆形态为方法,结合项目案例,从目标定位、文化阐释、资源整合、编排展示、技术支撑角度构建“五位一体”的实践路径。

数字人才与中小企业开放式创新绩效:一个中介调节效应模型

随着数字经济的深入发展,人才在数字化转型过程中的作用得到了学术界和企业界的大量关注。在数字化转型背景下,数字人才如何影响开放式创新缺乏深入探讨。立足数字化赋能的视角,基于“数字人才—知识搜索—开放式创新”的基本逻辑,构建了一个有中介的调节效应模型。在此基础上,利用1930家中小企业的调研数据,对提出的理论模型进行实证检验。研究发现:数字人才有助于改善中小企业开放式创新绩效;数字人才通过知识搜索广度和知识搜索深度正向影响中小企业开放式创新绩效,知识搜索广度和知识搜索深度对中小企业开放式创新绩效也有正向影响。同时,IT能力正向调节了数字人才通过知识搜索广度和知识搜索深度对中小企业开放式创新绩效关系。研究结论对中小企业如何提升开放式创新绩效以及如何应对数字化转型都具有重要指导意义。

多层次矿床本体的构建及在知识图谱中的应用

将具有多元异构性和复杂语义的矿床数据转化为结构化数据,是目前矿产资源勘查大数据领域面临的关键问题。传统的机器学习方法无法精确描述实体概念、属性及其属性值的语义信息,导致多源异构数据的可解释性较差。因此,可解释性的知识图谱已成为当前研究的热点。然而,当前矿床领域本体构建研究仍相对匮乏,这阻碍了矿床知识图谱的研究。本文聚焦于矿床领域的概念、关系、属性描述,结合知识工程、叙词表、复用前人本体及专家知识,采用基于知识工程和基于顶层本体相结合的本体构建方法,使用本体开发工具Protégé构建了以时空矿床文本为基础的矿床领域本体库,实现了矿床知识概念、关系的系统化、规范化、形式化表达。然后运用Neo4j构建本体库知识图谱,并以庞西垌多金属矿床为案例,将矿床本体与矿床数据进行了知识图谱连接,展示了矿床本体作为知识图谱骨架的重要性。本文研究对下一步矿床知识图谱推理分析具有一定的指导意义。

市场动荡性如何影响企业外部知识搜寻:管理创新的中介作用

环境会影响企业外部知识搜寻,然而有关组织层面中介机制的探讨较少。考虑到企业变革需要兼顾有效性与合法性,基于管理创新的理性视角与制度视角,探究其如何为外部知识搜寻建立组织体系,并将其标准化与制度化。以120个高管的自我报告为样本数据,进行实证分析。研究结果显示,市场动荡性通过管理创新的2个子维度——组织结构创新、流程创新间接影响外部知识搜寻。其中,组织结构创新与流程创新起完全中介作用,组织冗余调节市场动荡性与管理创新间的正向关系。