GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text.To better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic classification.In the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input.In the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be diagnosed.To differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text subgraph.Themessage-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process.Finally,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results.Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation

Based on the well logging knowledge graph of hydrocarbon-bearing formation(HBF),a Knowledge-Powered Neural Network Formation Evaluation model(KPNFE)has been proposed.It has the following functions:(1)extracting characteristic parameters describing HBF in multiple dimensions and multiple scales;(2)showing the characteristic parameter-related entities,relationships,and attributes as vectors via graph embedding technique;(3)intelligently identifying HBF;(4)seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation.Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin,NW China as objects,80%of the wells were randomly selected as the training dataset and the remainder as the validation dataset.The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43%with the expert interpretation results and a coincidence rate of 84.38%for all the oil testing layers,which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation.In addition,a number of potential pays likely to produce industrial oil were recommended.The KPNFE model effectively inherits,carries forward and improves the expert knowledge,nicely solving the robustness problem in HBF identification.The KPNFE,with good interpretability and high accuracy of computation results,is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.

基于CiteSpace的智能纺织服装产品研究进展

为揭示智能纺织服装的研究现状、热点和趋势,以CNKI数据库和WOS数据库中2012—2022年的相关文献为研究对象,采用CiteSpace可视化分析软件绘制知识图谱,分析智能纺织服装产品在国家、机构、作者方面的分布现状及合作情况,对研究热点和前沿趋势进行梳理。结果表明,2012—2022年,CNKI数据库发文量稳步上升,WOS数据库发文量快速增长且中国学者发文量较多;CNKI数据库研究合作强度较低且以东华大学和江南大学为主体;WOS数据库合作关系密切,研究集中度和合作强度大;CNKI数据库研究方向更注重应用和设计模式,WOS数据库则更注重电子元件制造和智能纤维与面料的开发;健康检测、变色肌理、仿生设计、生物材料、可穿戴应变等是目前智能纺织服装领域的前沿热点,电子元件制造、智能纤维及面料智能化研究是未来智能纺织服装领域研究的热点及趋势。

国外ChatGPT领域研究现状、热点及启示

作为新一代人工智能技术,ChatGPT火爆全网,成为全球的研究热点之一。综合运用知识图谱可视化分析与统计分析方法,以CiteSpace和Excel为分析工具,以Web of Science核心合集数据库的102篇学术成果作为分析对象,对国外ChatGPT研究领域进行全面深入分析。研究发现:国外ChatGPT研究始于2023年初,研究热度较高,但仍处于研究初期;该研究缺乏核心作者群及稳定合作团队,尚未受到期刊、基金的重视,且中国学者在全球范围的影响力不高;目前多聚焦在医学、教育学及计算机科学领域,其他学科领域虽有涉及,但整体关注度仍不高;研究热点除在ChatGPT本体及相关技术方面外,更多聚焦在医学、教育、学术研究与出版等领域。基于此,国内在重视ChatGPT研究基础上,更应通过资金扶持及其他多种激励措施加以刺激,加快扶持一批高产作者及团队,并鼓励其在更多领域进行研究与应用,进而提升国内ChatGPT研究成果及国际影响力。

AI视域下图书馆视听资源智慧化加工的探索

新技术环境下网络视听服务蓬勃兴起,深刻影响和推动公共文化服务行业的变革与发展。视听资源是图书馆立体馆藏资源体系的重要组成部分,与图书馆传统的平面化文献资源相比,其立体化的资源呈现形式更具感染力、亲和力和传播力。论文探讨人工智能技术在图书馆视听资源的应用场景、实现功能,特别对人工智能技术在视听资源内容自动化识别、知识内容发现、知识体系构建、知识关联服务等方面的探索与应用进行了探讨,以期为其他机构在拓展智慧图书馆多维服务领域上开辟新的应用范围提供思路与参考。

基于CiteSpace的地质大数据与人工智能研究热点及前沿分析

为研究地质学领域的大数据和人工智能研究现状、热点和前沿,在中国知网(CNKI)核心期刊和Web of Science(WoS)核心数据库收集了2000—2022年相关中文文献3600篇、英文文献1803篇,利用社区结构分析软件CiteSpace,从合作作者、研究国家、研究机构、关键词聚类、关键词时空分布图谱等进行可视化分析,并统计了2021—2022年间,地质学领域国际顶级期刊(综合影响因子10以上)的文献进行前沿分析。分析结果表明,近10年内该研究领域全球累计发文量激增,以中国为代表的亚洲国家和以美国为代表的欧美国家研究为主,双方累计发文量相差不大,论文中介中心性欧美国家普遍较高。我国研究机构之间的交流合作居多,与国外的研究机构交流合作较少,国外研究机构则与之相反。该领域以应用机器学习类方法、知识图谱构建等,在地质灾害防治、地震解释、石油与天然气勘查、固体矿产资源预测等方向进行的科学研究为研究热点,以深度学习、集成学习、智能平台搭建等为手段的地球演化过程中的重大地质事件研究、全球性气候变化、极地及海洋地质研究、数字地质建模及定量分析、地震预报、地灾易发性精准评估等为研究前沿。

基于CiteSpace知识图谱的“人工智能+图书馆”研究进展分析

以2014—2023年中国知网(CNKI)数据库核心期刊文献为样本,通过CiteSpace可视化软件,对“人工智能+图书馆”领域的研究现状与热点进行分析。总结该领域的研究热点和发展趋势,为进一步优化“人工智能+图书馆”领域的未来发展及知识构建提供参考及借鉴。

Forestry big data platform by Knowledge Graph

Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

1989—2022年生态环境中人工智能应用的研究综述——基于Citespace的知识图谱分析

人工智能是研究用计算机来模拟人的某些思维过程和智能行为的学科,近年来在生态环境业务领域的应用价值逐渐凸显。基于1989—2022年间CNKI收录的6732篇中文文献和WOS核心合集中5012篇英文文献,使用Citespace可视化分析软件,采用传统文献梳理与计量方法,对生态环境中人工智能的知识基础、前沿热点、发展趋势进行梳理,总结了国内外发展脉络、研究前沿、路径演化和最新进展。结果表明:国外在环境风险评估方面研究较多,关注空气污染、水质量等与人体健康密切相关的层面;国内对环境监管的研究居于前列,研究内容多是人工智能在城市街区空气质量、交通噪声、水质、土地覆盖等方面的应用。国内研究方法涵盖水质预测模型、大气污染物扩散模拟、土壤污染算法预测等应用方法,迁移学习、深度学习、机器学习、强化学习等理论方法,国外重点关注深度学习、机器学习、人工神经网络等理论方法的应用。国外近6年一贯延续用机器学习研究水与空气质量这两类对象,国内近3年对机器学习、深度学习等研究方法的关注热度逐渐凸显。

Intelligent acupuncture:data-driven revolution of traditional Chinese medicine

Acupuncture,a form of traditional Chinese medicine with a history of 2,000 years in China,has gained wider acceptance worldwide as a complementary therapy.Studies have examined its effectiveness in various health conditions and it is commonly used alongside conventional medical treatments.With the development of artificial intelligence(AI)technology,new possibilities for improving the efficacy and precision of acupuncture have emerged.This study explored the combination of traditional acupuncture and AI technology from three perspectives:acupuncture diagnosis,prescription,and treatment evaluation.The study aimed to provide cutting-edge direction and theoretical assistance for the development of an acupuncture robot.

基于ChatGLM3-6B的煤矿事故知识图谱构建及应用研究

为推动煤矿领域知识图谱构建及应用向自动化、信息化、智能化方向发展,解决传统方法存在大量标注数据、费时费力、识别效果低等问题,提出基于ChatGLM3-6B的煤矿事故知识图谱的构建方法。通过构建提示模板,使用ChatGLM3-6B模型的上下文学习能力,进行知识抽取以及Neo4j图数据库存储。采用LangChain框架,实现针对煤矿事故知识图谱的智能查询问答。研究结果表明:ChatGLM3-6B在单样本提示和少样本提示学习策略下信息抽取效果都要高于Lattice LSTM,Bert-CRF和RoBERTa-CRF模型,在F 1值上分别达到0.8010,0.8537,其次在知识问答模块,通过煤矿事故问答案例实验测评发现,达到预期应用效果。研究结果不仅可为煤矿安全领域提供1种高效智能的事故知识图谱构建与应用技术,还可为煤矿管理者在事故防控方面的安全决策提供辅助支持。

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.

我国体育人工智能领域研究的演进、热点与前沿——基于CiteSpace知识图谱的可视化计量分析

体育人工智能领域的发展对于我国实现体育强国、科技强国及健康中国战略具有重要的理论价值与现实意义。研究对我国体育人工智能领域研究的演进、热点与前沿进行整体把握与分析,以期为该领域科学研究和实践应用提供参考。以中国知网(CNKI)作为研究文献的数据平台,通过CiteSpace软件对我国体育人工智能领域384篇高质量文献进行知识图谱的可视化计量分析。结果表明:(1)该领域首次出现高质量文献为1992年,1992—2005年处于缓慢发展阶段,2006—2016年处于波动变化阶段,2017年至今处于蓬勃发展阶段。作者及作者团队之间合作十分欠缺,尚处于各自为政的松散状态。研究机构合作主要是“体育类高校为主,师范类高校和科研院所为辅,综合类高校为补充”的模式。研究机构间合作较少,仍有很大改进空间。(2)通过关键词共现和关键词聚类分析发现,我国体育人工智能重点研究领域为“体育产业”和“竞技体育”聚类;研究热点为“数据挖掘”和“预测模型”方向。“体育产业”聚类内体育经济、数字经济和体育产业方向为前沿的研究热点;“竞技体育”聚类内竞技体育、学校体育和全民健身为前沿的研究热点。(3)通过时间线图谱、关键词突现图谱、时区图谱和峰峦图谱分析发现,初期(1992—2005年为初步探索阶段),研究热点及前沿集中于人工智能、数据挖掘、体育、预测模型、预测、学习和神经网络。中期(2006—2016年处于稳定发展阶段),研究热点及前沿主要集中于体育产业,其他研究方向较为分散。后期(2017年至今为研究深化阶段),随着国家一系列政策的出台,体育人工智能上升到国家战略层面,此阶段的研究全面且深入,研究热点及前沿集中于体育赛事、体育成绩、预测模型、神经网络、体育新闻、人工智能、核心素养、体育产业、数字经济、深度学习、大数据、元宇宙、数字技术、体育教育方向的研究。

我国教育人工智能研究热点及趋势——基于CiteSpace和VOSviewer的可视化分析

为了促进人工智能在教育领域的深度应用,加快实现人工智能为教育赋能,以“教育+人工智能”为主题对中国知网(CNKI)的CSSCI数据库进行检索,最终筛选出1395篇文献作为研究样本,利用CiteSpace和VOSviewer工具进行全面分析,发现目前我国教育人工智能的研究主要包括人工智能与教育理论的探讨、人工智能与教育的技术研究、人工智能在教育领域的应用以及人工智能对教育的影响。未来,研究还需强化理论基础,加强技术突破,注重实证研究和关注伦理问题。

Expert Knowledge-Based Apparel Recommendation Question and Answer System

Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.

基于CiteSpace的人工智能伦理风险研究进展

利用Web of Science和中国知网的数据资源,采用Citespace知识图谱的可视化技术,总结了当前全球范围内关于人工智能伦理风险的研究动态。结果表明:1.2014-2024年国内外人工智能伦理风险研究文献数均经历了由缓慢到迅速增长的过程;2.在学术交流中,众多国内外研究者和机构已构建了合作网络系统,中国的发文量与国际水平相比处于追赶阶段;3.通过关键词可视化分析及其聚类可见,人工智能伦理风险研究长期聚焦在教育学、医学、心理学等领域;4.国内外研究重点存在差异,国内研究更侧重于教育伦理、伦理风险、伦理治理等方面,而国外研究则更关注医疗伦理、心理健康、技术应用等议题;5.国内研究从技术发展关注转向伦理规范探讨,重视人机共生与价值共识。国外则从构建AI伦理框架到算法优化、风险管理,关注生成式AI在个体和家庭的应用。对当前国内人工智能伦理风险研究的启示包括:本土化伦理框架、强化跨学科合作、提升国际交流、重视隐私保护、平衡技术创新影响。

人工智能(AI)+知识图谱在混合式教学中的应用

随着国家经济的发展,为满足国家对于人才的需求,对于教育领域而言,就要不断对人才进行培养,而为满足人才培养的需求,则需对教育教学进行不断改革。文章从当今教育数字化的发展角度出发,首先研究人工智能(AI)与知识图谱的发展背景,然后分别对人工智能(AI)赋能教学的方式与应用、知识图谱的构建与应用进行详细论述,从而进一步探讨基于人工智能(AI)+知识图谱的混合式教学模式的构建,以期满足学生的个性化学习需求,提高教与学的质量。

融合群体智能策略的AI链在大坝防汛抢险知识推理中的应用

防汛抢险知识(实体、关系)是防汛抢险业务知识图谱的重要组成部分。防汛实体间关系错综复杂分布在无结构文本中,而可利用文本数量过少和文本质量偏低为该领域知识抽取工作带来了挑战。为此本文提出使用大型语言模型LLM(Large Language Model)进行大坝防汛抢险知识推理的思路。基于LLM设计防汛实体抽取器、防汛实体知识解析器以及防汛实体间关系决策器三个子模块,设计一系列有效任务提示,并将其链接形成人工智能AI(Artificial Intelligence)链。通过AI链中提示与LLM实时交互逐步完成防汛知识推理任务。同时设计群体智能策略提高防汛实体间关系推理的可靠性。将本文提出的知识推理方法与现有方法进行对比,实验结果表明本文设计的AI链可有效提升LLM进行实体间关系推理的准确率,验证了AI链和群体智能策略的有效性。这一知识推理新范式可为提高水利防汛知识可访问性提供新的解决思路。