How to implement a knowledge graph completeness assessment with the guidance of user requirements

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

Classification of knowledge graph completeness measurement techniques

At present,although knowledge graphs have been widely used in various fields such as recommendation systems,question and answer systems,and intelligent search,there are always quality problems such as knowledge omissions and errors.Quality assessment and control,as an important means to ensure the quality of knowledge,can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time.Therefore,as an indispensable part of the knowledge graph construction process,the results of quality assessment and control determine the usefulness of the knowledge graph.Among them,the assessment and enhancement of completeness,as an important part of the assessment and control phase,determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities.In this paper,we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions,open world assumptions,and partial completeness assumptions.The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Optimization of Agricultural Industrial Structure in Changping District of Beijing Based on Grey Relational Analysis

In the economic development of Beijing,although the share of the total amount of agricultural industry in the overall economy is relatively low,it has an important impact on the daily life of residents,social stability and the development of other industries.Changping District,as an important agricultural production base of Beijing,its agricultural development has an indispensable strategic significance for the stability and growth of the entire regional economy.Therefore,it is very important to study the structure of agricultural industry in Changping District.Based on the detailed analysis of the agricultural industrial structure of Changping District,this paper uses the grey relation theory to analyze the different industries in the agricultural industrial structure of Changping District,including planting,forestry,animal husbandry,fishery and agricultural,forestry,service industries,in order to reveal the impact of these industries on the agricultural industrial structure of Changping District.Through this study,it comes up with specific and feasible suggestions for the optimization of agricultural industrial structure in Changping District,and provides valuable reference for the agricultural development of other areas in Beijing.

Optimization of short-circuit transition parameters of Q235B steel GMAW based on orthogonal gray relational analysis

In gas metal arc welding(GMAW)process,the short-circuit transition was the most typical transition observed in molten metal droplets.This paper used orthogonal tests to explore the coupling effect law of welding process parameters on the quality of weld forming under short-circuit transition,the design of 3 factors and 3 levels of a total of 9 groups of orthogonal tests,welding current,welding voltage,welding speed as input parameters:effective area ratio,humps,actual linear power density,aspect ratio,Vickers hardness as output paramet-ers(response targets).Using range analysis and trend charts,we can visually depict the relationship between input parameters and a single output parameter,ultimately determining the optimal process parameters that impact the single output index.Then combined with gray the-ory to transform the three response targets into a single gray relational grade(GRG)for analysis,the optimal combination of the weld mor-phology parameters as follows:welding current 100 A,welding voltage 25 V,welding speed 30 cm/min.Finally,validation experiments were conducted,and the results showed that the error between the gray relational grade and the predicted value was 2.74%.It was observed that the effective area ratio of the response target significantly improved,validating the reliability of the orthogonal gray relational method.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

基于概率的震级完备度方法评估云南地震台网的监测能力

为了科学评估云南地震台网的监测能力,分析云南地区的地震监测能力时空分布特征,采用基于概率的震级完备度(PMC)方法,利用2009-2023年云南地震台网地震目录、震相和台站信息等数据,计算了云南地震台网在云南地区的平均地震监测能力和在不同深度的监测能力,得到滇中、滇西局部地区的基于概率的完备性震级M_(P)=1.5,云南全境基本都能监测到2.5级地震。评估了研究时段内云南地震台网台站监测能力的演变,发现部分台站的监测能力持续下降。但自2018年增加20个台站后,云南2.0及2.5级地震的监测范围有一定的扩大,但微震监测能力提升不明显,可能是因为2018年以来云南一直处于小震弱活动状态。最后,对应用PMC方法规划提升台网监测能力进行了展望。

新质生产力的运行机理、生成逻辑与模式建构——基于生产力与生产关系辩证统一的视角

新质生产力是创新发挥主导作用,摆脱传统经济增长方式、生产力发展路径,具有高科技、高效能、高质量特征,符合新发展理念的先进生产力质态。纵观三次科技革命变革生产力的历程,新质生产力的产生和发展是生产力与生产关系相互作用的结果,具有生产力与生产关系的双重内涵和发展要求。新质生产力以新型劳动者、新型劳动资料、新型劳动对象及其组合模式和新型生产关系为架构要素与发展基础,其生成逻辑与运行机理是由技术革命性突破引发,在催生新型生产力要素、新型要素组合模式的基础上促使与新质生产力相适应的新型生产关系变革形成,新型生产关系的反作用促进新质生产力进一步生成的过程。发展新质生产力要以实施创新驱动战略、加快产业深度转型、构建要素生态系统和全面深化改革为基本模式和关键路径。亟须加快实现高水平科技自立自强,加快技术革命性创新;促进产业深度转型,构建现代化产业体系;促进生产要素创新配置,构建新质生产要素系统;全面深化改革,加快建立健全与新质生产力相适应的新型生产关系,促进新质生产力加快形成发展。

基于双目视觉技术的茶叶嫩芽定位方法研究

进一步提升茶叶嫩芽定位的准确性及茶叶嫩芽采摘的作业效率,选取双目视觉技术作为切入点,针对其识别、定位、采摘等环节展开设计研究。考虑茶叶嫩芽采摘的基础特征和双目视觉技术的应用条件,合理搭建双目视觉模型,选用立体匹配的核心定位算法,完成茶叶嫩芽的定位方法与采摘功能优化,并布置相应的试验机构,展开定位采摘验证试验。结果表明:基于双目视觉技术的茶叶嫩芽定位方法应用具有显著的优越性,较普通定位方法而言,识别误匹配率可降低至4.01%,平均漏采率为3.20%,嫩芽采摘完整率达93.40%,可以很好地促进茶叶嫩芽的后续处理加工,是采摘茶叶嫩芽的品质化的重要基础保障。

铀对α-Al_(2)O_(3)中氢与本征点缺陷相互作用的影响

在长期高温服役过程中,铀床的氚渗透泄露问题是聚变堆涉氚系统中需要关注的焦点之一,而一种行之有效的解决思路是在内壁制备阻氚涂层.然而,服役过程中通过热扩散渗入涂层内部的铀可能在阻氚涂层氢行为中起作用,进而影响其服役可靠性.基于此,采用第一性原理计算方法,研究了铀对α-Al_(2)O_(3)中氢与本征点缺陷相互作用的影响.结果发现,铀对α-Al_(2)O_(3)中空位型本征点缺陷及氢相关缺陷的存在形式、电荷态及相对稳定性有重要影响.从形成能观点来看,铀对α-Al_(2)O_(3)中空位型本征点缺陷及氢相关缺陷的形成有利,且α-Al_(2)O_(3)中空位型点缺陷对氢的捕陷能力因铀的引入显著增加.研究结果对α-Al_(2)O_(3)基内壁阻氚涂层在氚工艺系统铀床中应用的可靠性评估具有重要的指导意义.

Advancements in Time Modeling: Relationalism, Divisional Structures, and Geometry

This article broadens terminology and approaches that continue to advance time modelling within a relationalist framework. Time is modeled as a single dimension, flowing continuously through independent privileged points. Introduced as absolute point-time, abstract continuous time is a backdrop for concrete relational-based time that is finite and discrete, bound to the limits of a real-world system. We discuss how discrete signals at a point are used to temporally anchor zero-temporal points [t = 0] in linear time. Object-oriented temporal line elements, flanked by temporal point elements, have a proportional geometric identity quantifiable by a standard unit system and can be mapped on a natural number line. Durations, line elements, are divisible into ordered unit ratio elements using ancient timekeeping formulas. The divisional structure provides temporal classes for rotational (Rt24t) and orbital (Rt18) sample periods, as well as a more general temporal class (Rt12) applicable to either sample or frame periods. We introduce notation for additive cyclic counts of sample periods, including divisional units, for calendar-like formatting. For system modeling, unit structures with dihedral symmetry, group order, and numerical order are shown to be applicable to Euclidean modelling. We introduce new functions for bijective and non-bijective mapping, modular arithmetic for cyclic-based time counts, and a novel formula relating to a subgroup of Pythagorean triples, preserving dihedral n-polygon symmetries. This article presents a new approach to model time in a relationalistic framework.

Grey Relational Analysis between Chlorophyll a and Environmental Factors in ShaHu Lake

[Objective] The aim was to explore effects of environmental factors on the content of Chlorophyll a in ShaHu Lake.[Method] Based on the data in Shahu Lake from November in 2007 to September in 2008,the relationship between chlorophyll a and environmental factors like water temperature,pH,secchi-depth （SD）,total nitrogen,total phosphorus and potassium permanganate index was studied by grey relational analysis method.[Result] The main environmental factors affecting the content of Chlorophyll a in ShaHu Lake were in order of water temperature potassium permanganate index 〉total nitrogen 〉pH〉 total phosphorus 〉SD.[Conclusion] The research provides reference for the control of eutrophication and the reasonable development and utilization of Shahu Lake.

Insulation fault diagnosis based on group grey relational grade analysis method for power transformers

Utilising dissolved gases analysis, a new insulation fault diagnosis methodfor power transformers is proposed. This method is based on the group grey relational grade analysismethod. First, according to the fault type and grey reference sequence structure, some typicalfault samples are divided into several sets of grey reference sequences. These sets are structuredas one grey reference sequence group. Secondly, according to a new calculation method of the greyrelational coefficient, the individual relational coefficient and grade are computed. Then accordingto the given calculation method for the group grey relation grade, the group grey relational gradeis computed and the group grey relational grade matrix is structured. Finally, according to therelational sequence, the insulation fault is identified for power transformers. The results of alarge quantity of instant analyses show that the proposed method has higher diagnosis accuracy andreliability than the three-ratio method and the traditional grey relational method. It has goodclassified diagnosis ability and reliability.

WEIGHTED ALGORITHM FOR FINGERPRINTING RELATIONAL DATABASES

The necessity and the feasibility of introducing attribute weight into digital fingerprinting system are given. The weighted algorithm for fingerprinting relational databases of traitor tracing is proposed. Higher weights are assigned to more significant attributes, so important attributes are more frequently fingerprinted than other ones. Finally, the robustness of the proposed algorithm, such as performance against collusion attacks, is analyzed. Experimental results prove the superiority of the algorithm.

Semantic-based query processing for relational data integration

To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.

A method for publishing relational schema into DTD

This paper focuses on exporting relational data into extensible markup language (XML). First, the characteristics of both relational schemas represented by E-R diagrams and XML document type definitions (DTDs) are analyzed. Secondly, the corresponding mapping rules are proposed. At last an algorithm based on edge tables is presented. There are two key points in the algorithm. One is that the edge table is used to store the information of the relational dictionary, and this brings about the efficiency of the algorithm. The other is that structural information can be obtained from the resulting DTDs and other applications can optimize their query processes using the structural information.

Hypidone hydrochloride(YL-0919)ameliorates functional deficits after traumatic brain injury in mice by activating the sigma-1 receptor for antioxidation

Traumatic brain injury involves complex pathophysiological mechanisms,among which oxidative stress significantly contributes to the occurrence of secondary injury.In this study,we evaluated hypidone hydrochloride(YL-0919),a self-developed antidepressant with selective sigma-1 receptor agonist properties,and its associated mechanisms and targets in traumatic brain injury.Behavioral experiments to assess functional deficits were followed by assessment of neuronal damage through histological analyses and examination of blood-brain barrier permeability and brain edema.Next,we investigated the antioxidative effects of YL-0919 by assessing the levels of traditional markers of oxidative stress in vivo in mice and in vitro in HT22 cells.Finally,the targeted action of YL-0919 was verified by employing a sigma-1 receptor antagonist(BD-1047).Our findings demonstrated that YL-0919 markedly improved deficits in motor function and spatial cognition on day 3 post traumatic brain injury,while also decreasing neuronal mortality and reversing blood-brain barrier disruption and brain edema.Furthermore,YL-0919 effectively combated oxidative stress both in vivo and in vitro.The protective effects of YL-0919 were partially inhibited by BD-1047.These results indicated that YL-0919 relieved impairments in motor and spatial cognition by restraining oxidative stress,a neuroprotective effect that was partially reversed by the sigma-1 receptor antagonist BD-1047.YL-0919 may have potential as a new treatment for traumatic brain injury.

Complete response of gallbladder cancer treated with gemcitabine and cisplatin chemotherapy combined with durvalumab:A case report and review of literature

BACKGROUND Gallbladder cancer(GBC)is the most common and aggressive subtype of biliary tract cancer(BTC)and has a poor prognosis.A newly developed regimen of gemcitabine,cisplatin,and durvalumab shows promise for the treatment of advanced BTC.However,the efficacy of this treatment for GBC remains unclear.CASE SUMMARY In this report,we present a case in which the triple-drug regimen exhibited marked effectiveness in treating locally advanced GBC,thus leading to a long-term survival benefit.A 68-year-old man was diagnosed with locally advanced GBC,which rendered him ineligible for curative surgery.Following three cycles of therapy,a partial response was observed.After one year of combined therapy,a clinical complete response was successfully achieved.Subsequent maintenance therapy with durvalumab monotherapy resulted in a disease-free survival of 9 months for the patient.The patient experienced tolerable toxicities of reversible grade 2 nausea and fatigue.Tolerable adverse events were observed in the patient throughout the entirety of the treatment.CONCLUSION The combination of gemcitabine and cisplatin chemotherapy with durvalumab was proven to be an effective treatment approach for advanced GBC,with manageable adverse events.Further research is warranted to substantiate the effectiveness of the combined regimen in the context of GBC.

中老年2型糖尿病患者合并认知功能障碍的相关因素

目的:探讨中年和老年两个年龄段的2型糖尿病(T2DM)患者合并认知功能障碍的相关因素。方法:2018年9月至12月在北京市一大型社区居民中筛选出970名T2DM患者(中年585例,老年385例)。用简易精神状态量表(MMSE)评估认知功能。使用多因素logistic回归进行相关因素分析。结果:认知功能障碍检出率中年组12.0%,老年组13.5%。在中年T2DM人群中,工作(OR=0.22,95%CI:0.03~0.77)和受教育程度为大专/本科及以上(OR=0.18,95%CI:0.04~0.55)是保护因素,合并心梗(OR=4.13,95%CI:1.26~13.63)是危险因素。在老年T2DM人群中,每周饮茶1~2次(OR=0.11,95%CI:0.01~0.58)和受教育程度为大专/本科及以上(OR=0.19,95%CI:0.05~0.54)是保护因素,合并脑卒中(OR=3.64,95%CI:1.55~8.39)和很好的睡眠自我评价(OR=2.75,95%CI:1.13~7.35)是危险因素。结论:中年T2DM患者合并认知功能障碍与工作、受教育程度和心梗有关,老年T2DM患者合并认知功能障碍与生活方式、受教育程度和脑卒中有关。