Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Incorporating Fuzzy Inference in Active Database Rules

Active databases react to stimulation, or event from inside or outside the system without user or application interference through Events Conditions Actions(ECA) rules (triggers). ECA rule is defined as: ON event IF condition THEN action, which means when an event happens, if the condition is satisfied then the corresponding action is executed. The nature of ECA rule makes it an appropriate means to model dynamic character of systems, as gained much studies during recent years. Traditional ECA rule is crisp, which means their events, condition (s) and action(s) are accurate. As indicate that ECA rules can only represent precise knowledge. But knowledge is usually fuzzy in engineering. A concept of fuzzy ECA rules characterized with fuzzy event, fuzzy condition and fuzzy action is proposed in this article.The realization avenues of fuzzy triggers are discussed. The work we have done blazes a way in representing approximate syntax in active database application systems. At last a case of 'overheating alarm' is given to illustrate the approach.

Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference

Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making.This paper studies an intention estimation method based on fuzzy theory,combining prob-ability to calculate the intention between two objects.This method takes a space object as the origin of coordinates,observes the target’s distance,speed,relative heading angle,altitude difference,steering trend and etc.,then introduces the spe-cific calculation methods of these parameters.Through calculation,values are input into the fuzzy inference model,andfinally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability.Ver-ified by simulation experiment,the target intention inferred by this method is roughly the same as the actual behavior of the target,which proves that the meth-od for identifying the target intention is effective.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Mapping of Freshwater Lake Wetlands Using Object-Relations and Rule-based Inference

Inland freshwater lake wetlands play an important role in regional ecological balance.Hongze Lake is the fourth biggest freshwater lake in China.In the past three decades,there has been significant loss of freshwater wetlands within the lake and at the mouths of neighboring rivers,due to disturbance,primarily from human activities.The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them.In the paper,an integrated method,which combined per-pixel and per-field classification,was used for mapping wetlands of Hongze Lake and their neighboring upland types.Firstly,Landsat ETM+ imagery was segmented and classified by using spectral and textural features.Secondly,ETM+ spectral bands,textural features derived from ETM+ Pan imagery,relative relations between neighboring classes,shape features,and elevation were used in a decision tree classification.Thirdly,per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context.The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies,but also has improved the accuracy of identification by nearly 5%.

The Jena-Based Ontology Model Inference andRetrieval Application

Ontology as an important representation model of semantic web has valuable application. A new ontology model on the basis of Computer Graphics (CG) knowledge is proposed, called CG ontology model. The protégé is used to build this ontology model conveniently. The Jena API is applied to store CG owl documents in MySQL, set inference rule and achieve search queries on the ontology database. Finally, the Jena-based ontology model retrieval system is developed.

Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management

In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.

Evolving Fuzzy Neural Networks for Extracting Rules

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面向溢流与井漏监测的钻井液池体积趋势校正方法

钻井液池体积是溢流和井漏监测的常用参数。在钻井正常施工时,随着井筒容积的变化以及井筒内钻井液与地层流体的交换,钻井液池体积会产生与溢流和井漏发生时相似的“类风险趋势”,导致现有的溢流与井漏监测方法易产生误报,降低了溢流与井漏监测方法在现场应用时的有效性。针对此问题,文章首先分析了导致池体积产生“类风险趋势”的原因,建立了钻进与起下钻工况下池体积的正常变化模型,利用建立的模型校正池体积的“类风险趋势”,降低溢流与井漏监测的误报率;然后,建立了基于规则推理的溢流与井漏监测模型,用于测试池体积的“类风险趋势”对溢流与井漏监测的影响。利用现场实测的4500组钻井数据,采用基于规则推理的溢流与井漏监测方法开展了风险监测实验,结果表明,在进行池体积趋势校正后,溢流与井漏风险监测的误报率由10.03%降低至3.06%。

煤矿事故智能应急预案生成方法研究

为了实现应急预案数字智能化管理,快速生成精准有效的救援方案,采用网络爬虫技术从事故案例及事发后状况中获取基础数据,并对数据进行结构化处理;构建一种分层结构化应急预案生成模型,结合模糊综合评价法与层次分析法对应急预案进行有效性评估,形成了煤矿事故数字智能化应急预案系统。选取煤矿瓦斯爆炸事故,验证“8·29”肖家湾煤矿瓦斯爆炸事故和“10·31”金山沟煤业瓦斯爆炸事故子事件集合,证明了智能应急预案生成结果和实际情况基本相符。研究结果表明:实时添加事发信息,快速生成可视化预案,自动分析结构化存储,可提高应急预案生成效率与精准性,降低事故灾害风险和损失,提升矿井应急救援保障能力。

基于改进概率软逻辑模型的高校学生状态推理方法

随着高等教育的普及,各类管理决策需求增加,基于数据驱动和知识驱动的决策方法成为新的发展趋势。基于此,提出了一种通过改进概率软逻辑模型挖掘知识规律和推理学生状态的方法,该方法通过引入规则挖掘算法,赋予概率软逻辑模型自动挖掘规则的能力。在真实学生状态数据上的实验结果表明,该方法在多项目标关系的推理上,准确率能达到80%以上,并且多层次推理结构的实验效果优于非层次推理结构的实验效果。对于学生状态类非完整知识库数据的挖掘和推理,该方法减少了人工工作量,降低了因人为认知偏差带来的不确定性。

Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

Inferring rules for adverse load combinations to crack in concrete dam from monitoring data using adaptive neuro-fuzzy inference system

The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature loads.As cracks es-pecially unstable cracks are of great danger to the safety of dams,it is critical to avoid extremely adverse load combinations during the dam operations to achieve the stability of cracks.Conventionally,the adverse load combinations have to be deter-mined empirically by experts based on specific dam site conditions.Therefore,it is attractive to apply quantitative instead of empirical methods to identify the adverse loading conditions.In this study,we employ an adaptive neuro-fuzzy inference sys-tem(ANFIS) to Chencun concrete dam.The ANFIS is able to help us build a relationship between the model inputs(reservoir water level and air temperature) and the model output(crack opening displacement).Based on this relationship,the rules of the adverse load combinations to the crack are generated directly from the monitoring data.The accuracy of the trained ANFIS is proved by comparing the modeling results and the monitoring data.Our work demonstrates that the ANFIS is a useful ap-proach for accurately recognizing the rules of the adverse load combinations that can be used in the knowledge base of dam safety expert system.

An open logic system admitting modification of inference rules

Recently, Li established an open logic system to describe growth and updating of knowledge and evolution of hypothesis. Afterwards, he used it to give a logical framework for knowledge base maintenance. In a knowledge base, there are some special

Improving Rule Base Quality to Enhance Production Systems Performance

Production systems have a special value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. Therefore, it is of high importance to consider different techniques to improve their performance. In this research, rule base is the component of the production system that we aim to focus on. This work therefore seeks to investigate this component and its relationship with other components and demonstrate how the improvement of its quality has a great impact on the performance of the production system as a whole. In this paper, the improvement of rule base quality is accomplished in two steps. The first step involves re-writing the rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be obtained easily. The second step involves augmenting the rule base with inference short-cut rules devised from the long inference chains. These inference short-cut rules have a great impact on the performance of the production system. Finally, simulations are performed on randomly generated rule bases with different sizes and goals to be proved. The simulations demonstrate that the suggested enhancements are very beneficial in improving the performance of production systems.

Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules

Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles.Moreover,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried out.Therefore,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget sheets.In addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph.Finally,several root problems that led to vicious cycles were detected.Urban-problem experts evaluated the extracted causal relations.

On the inference rules in legal logic

推论规则是逻辑学研究的核心内容。法律逻辑是一门应用逻辑，但它并不是形式逻辑的推论规则在法律领域中的简单运用，而是形式逻辑的推论规则加上法律领域中的特殊推论规则的结果。因此，法律逻辑中的推论规则与形式逻辑中的推论规则相比，既有共性的一面，又有个性的一面。其共性表现为形式逻辑的基本推论规则是构成法律逻辑中的推论规则必不可少的组成部分，其个性表现为法律逻辑中有着与形式逻辑中的推论规则所不具有的特殊推论规则——证明责任规则。

A NEW EVIDENCE UPDATING RULE BASED ON CONDITIONAL EVENT

Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.

基于置信规则的村镇应急避难场所暴雨灾害链风险诊断

为解决村镇应急避难场所面临暴雨灾害时的风险问题,基于故障树和置信规则库推理方法,提出暴雨灾害链和村镇应急避难场所功能破坏链相结合的场所避难功能失效风险诊断模型。根据事故致因理论推理灾害节点变量,通过故障树描述灾害链,运用关联规则从历史灾害数据中挖掘规则,建立置信规则库系统,构建村镇应急避难场所功能失效风险诊断模型,并以四川省某寄宿制学校为例进行模型验证。研究结果表明:该模型可实现不同证据组合下村镇应急避难场所功能失效风险的诊断推理;实例的模型诊断结果与实际情况吻合,证实该模型能够科学地诊断村镇应急避难场所面临暴雨等恶劣自然条件时存在的风险,可为村镇应急避难场所规划设计和应急管理提供理论支撑。

多层次传递型内幕交易责任认定的实证考察与理论反思

信息传递与互联网技术的耦合带来传递型内幕交易中传播链条的延长,造成现行推定规则在追究各信息受领主体责任时的局限性,也导致了法律责任在交易端与泄露端间的严重不匹配。应当认识到,泄密人与受密人责任是成立内幕交易责任的两个方面,现行推定规则实则是在泄密人责任成立的基础上对受密人责任的推定。将泄密人违法性具体化为从泄密中获益,受密人交易行为的违法性具体化为对受密人违法的认知,方能揭示内幕交易以获益为核心的违法性本质。身份关系所蕴含的人际利益是信息传递的原动力,在坚持一次推定的基础上,通过分析信息传递过程中各主体间的身份关系,以信息获取便利性与利益一致性为实质审查标准,可以将形式上处于不同传播层级的主体实质认定为同一层级知情人,从而在确保推定事实效力的基础上对内幕交易责任主体实现精准覆盖。