Knowledge representation and rule-based solution system for dynamic programming model

A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

The Life Cycle of Knowledge in Big Language Models:A Survey

Knowledge plays a critical role in artificial intelligence.Recently,the extensive success of pre-trained language models(PLMs)has raised significant attention about how knowledge can be acquired,maintained,updated and used by language models.Despite the enormous amount of related studies,there is still a lack of a unified view of how knowledge circulates within language models throughout the learning,tuning,and application processes,which may prevent us from further understanding the connections between current progress or realizing existing limitations.In this survey,we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods,and investigating how knowledge circulates when it is built,maintained and used.To this end,we systematically review existing studies of each period of the knowledge life cycle,summarize the main challenges and current limitations,and discuss future directions.

Standard model of knowledge representation

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representa- tion model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

COMPUTER PATTERN DESIGN AND PAINTING TECHNIQUE BASED ON AESTHETICS KNOWLEDGE

Computer Art (CA) is a very important field in computer applications. Based on the analysis and summarization of the painting process, a new method of CA creation using the techniques of com- puter graphics and the expert system is presented in this paper. The reduction of pattern model, simu- lation of special effect, representation of aesthetics knowledge and fuzzy judgement of beauty are includ- ed by this new method.

SEMANTIC CONSTRAINT MODELER FOR 2D AND 3D GEOMETRY

Design changes for 2D & 3D geometry are the most important features in the process of product design.Constraint modeling for variationl geometry based on geometric reasoning is one of the best approaches for this goal.However,it is difficult for the proposed systems to maintain or handle the consistency and completeness of the constraint model of the design objects.To change this situation,a semantic model and its control approach are presented,aiming at the integration of the data,knowledge and method related to design objects.Aconstraint definition system for in- teractively defining the semantic model and a prototype modeler based on the semantic model are also implemented to examine the idea which is extended to 3D geometric design too.

A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic relations.Finally,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

Extension Modeling Strategy of Intelligent Detection in D.huoshanense Photosynthesis Process

Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage structure of EKR were presented. An Extension Solving Model (ESM) based on EKR was discussed in detail, including creation of the extension constraint graph, extended inference, calculation of relevant functions and generation of extension set. A knowledge base system based on EKR and ESM was developed, which was applied in extension repository system intelligent design of detection in photosynthesis process of D.huoshanense. More reasonable results were obtained than traditional rule-based system. EKR was feasible in intelligent design to solve the problem of intelligent detection knowledge representations.

PRODUCT MODELING AND PROCESS PLANNING WITHIN CONCURRENT ENGINEERING ENVIRONMENT

A prodouct modeling and a process planning that are two essential basses of realizing concurrent engineering are investigated , a logical modeling technique , grammar representation scheme of technology knowledge and architecture of expert system for process planning within con- current engineering environment are proposed. They have been utilized in a real reaserch project.

Geoscience knowledge graph in the big data era

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

IMIMMS──An Interactive Modelling and Intelligent Model Management Systems

This paper describes an Interactive Modelling and Intelligent Model Management System─IMIMMS.In the system, like-natural English stances can be understood,then they are interpreted into the WFF(Well-Formed Formula) as the goals of inference on modelling knowledge.The model is represented as the Predicates and Relational Framework (PRF).The IMIMMS is a problem -oriented system. Therefore, the model frame of solving problem can be generated during the iterative process. The IMIMMS implements intelligent model management and traces the environment change in a decision domain. Finally, the paper shows the IMIMMS framework and an example of iterative modelling.

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Dyspnea is one of the most common manifestations of patients with pulmonary disease,myocardial dysfunction,and neuromuscular disorder,among other conditions.Identifying the causes of dyspnea in clinical practice,especially for the general practitioner,remains a challenge.This pilot study aimed to develop a computeraided tool for improving the efficiency of differential diagnosis.The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data.Differential diagnosis approaches were established and optimized by clinical experts.The artificial intelligence(AI)diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor.Twenty-eight diseases and syndromes were included in the disease set.The model contained 132 variables of symptoms,signs,and serological and imaging parameters.Medical records from the electronic hospital records of Suining Central Hospital were randomly selected.A total of 202 discharged patients with dyspnea as the chief complaint were included for verification,in which the diagnoses of 195 cases were coincident with the record certified as correct.The overall diagnostic accuracy rate of the model was 96.5%.In conclusion,the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

Grey Qualitative Modeling and Control Method for Subjective Uncertain Systems

A new approach to model and control an unknown system using subjective uncertain rules is proposed. This method is established by combining the grey system theory and the qualitative simulation method. The proposed approach mainly contains three steps. In the first step, subjective uncertain rules are accumulated gradually during cognizing the system; the mapping relations between the system inputs and outputs are built and represented using the grey qualitative matrix in the second step; in the third step,the generalized whitening function is defined to realize the transformation between qualitative and quantitative information. Besides the theoretical results, two sets of simulations based on a water level control system are conducted comparatively to demonstrate the effectiveness of the proposed method. The water level expectation is set to be constant in the first set, while it changes in the second set. The simulation results show that the proposed method tracks the water level expectation well. By combining the proposed method with proportional-integral-derivative(PID) or fuzzy logic controller(FLC), it can be concluded that the system can reach the stable state more quickly and the overshoot can also be reduced compared to using PID or FLC alone.

竞技运动专家的认知优势及其形成机制——基于自动性特点和抽象化表征

目前对于竞技运动专家如何从长期训练中获得和迁移、提炼和更新专业知识与技能的机制尚未有清晰的阐释。基于专家-新手范式的研究表明,从表征学习的角度看竞技运动专家的认知优势主要体现在注意优势和记忆优势两方面,而竞技运动专家知识与技能的自动性特点和抽象化表征是其认知优势效应形成的两个关键原因。从生成模型的角度探讨竞技运动专家知识系统的抽象化表征机制,为能更好地理解竞技运动专家的认知优势效应,助其突破认知局限性提供了新的理论依据。

融合知识图谱和图注意力网络的旅游推荐算法

为缓解旅游推荐模型面临的数据稀疏和冷启动的问题,提出一种融合知识图谱和图注意力网络的旅游推荐算法KRGAT(knowledge ripple graph attention network)。借助水波网络从用户的历史旅游行为和知识图谱中挖掘用户偏好增强用户特征表示,针对当前旅游项目特征学习的方法难以提取节点深层特征的问题,利用图注意力网络聚合相关度更高的邻居节点信息,增强旅游项目特征表示。实验在自建立的旅游数据集上与5个基线方法进行对比,其结果表明,KRGAT的精确率(P)、召回率(R)和AUC值分别提升了5.73%、4.42%和1.42%。

基于业务流程的认知图谱

针对目前软件项目开发过程中无法充分利用已有业务资源,进而导致开发效率低、能力弱等问题,通过研究业务资源之间的关联,提出一种基于业务流程的认知图谱。首先,通过正式文档抽取业务知识,提出建立知识层级的方法并修正;其次,通过代码特征挖掘与代码实体相似度判断构建代码网络表示模型;最后,利用实际业务数据进行实验验证,并与向量空间模型(VSM)、多样化排序和深度学习等方法进行对比。最终构建的基于业务流程的认知图谱在代码检索方面优于目前基于文本匹配的方法和深度学习算法,分别在前5准确率(precision@5)、平均精度均值(mAP)、归一化折扣增益值(?-NDCG)这3项指标上高过多样化排序的代码检索方法4.30、0.38和2.74个百分点,有效解决了潜在业务词汇识别、业务认知推理表示等多个问题,提升了代码检索效果与业务资源利用率。

面向信息公开的突发公共卫生事件知识表示模型构建

近年来,突发公共卫生事件频发给政府应急管理带来了极大挑战。政府及时公开事件信息有助于消除公众恐慌,对于疫情防控和社会经济发展至关重要。然而,当前突发公共卫生事件信息以碎片化、不连续、不完整方式散落在不同位置,未能较好地集成融合。如何对海量、多样、变化的信息进行描述与组织是政府应急管理的关键。因此,本文面向信息公开,融合知识图谱和事理图谱,构建突发公共卫生事件知识表示模型,并对其中的核心概念及关系进行表示,提出了一种既能刻画疫情时空演化状态,又能展示疫情信息的突发公共卫生事件信息组织方法。研究结果显示,本文构建的知识表示模型具有较好的垂直性质,能够展示较为丰富的概念关系和属性特征,大部分类能够得到实例填充。本文提出的知识表示模型为突发事件知识库构建提供新思路,扩充了突发事件信息组织的方法体系,有助于存量信息公开与价值释放,进一步满足公众的信息需求,提升应急管理效果。