Construction of carbonate reservoir knowledge base and its application in fracture-cavity reservoir geological modeling

To improve the efficiency and accuracy of carbonate reservoir research,a unified reservoir knowledge base linking geological knowledge management with reservoir research is proposed.The reservoir knowledge base serves high-quality analysis,evaluation,description and geological modeling of reservoirs.The knowledge framework is divided into three categories:technical service standard,technical research method and professional knowledge and cases related to geological objects.In order to build a knowledge base,first of all,it is necessary to form a knowledge classification system and knowledge description standards;secondly,to sort out theoretical understandings and various technical methods for different geologic objects and work out a technical service standard package according to the technical standard;thirdly,to collect typical outcrop and reservoir cases,constantly expand the content of the knowledge base through systematic extraction,sorting and saving,and construct professional knowledge about geological objects.Through the use of encyclopedia based collaborative editing architecture,knowledge construction and sharing can be realized.Geological objects and related attribute parameters can be automatically extracted by using natural language processing(NLP)technology,and outcrop data can be collected by using modern fine measurement technology,to enhance the efficiency of knowledge acquisition,extraction and sorting.In this paper,the geological modeling of fracture-cavity reservoir in the Tarim Basin is taken as an example to illustrate the construction of knowledge base of carbonate reservoir and its application in geological modeling of fracture-cavity carbonate reservoir.

Research and Design of Parallel Knowledge Base Machine- PKBM95

Parallel Knowledge Base Machine PKBM95 is a kind of special computer which is designed to improve the inference capability of production systems. Its hardware architecture is a multiprocessor, consisting of one microcomputer and four TRANSPUTERs. We will focus our discussion on the concentration-scattered inference model and the twice-conflict resolution strategy presented in the this paper, as well as the architecture and operating language of PKBM95. According to experiments, they are effective in improving the inference capability of the system.

Efficient Large Language Model Application Development: A Case Study of Knowledge Base, API, and Deep Web Search Integration

This paper presents a reference methodology for process orchestration that accelerates the development of Large Language Model (LLM) applications by integrating knowledge bases, API access, and deep web retrieval. By incorporating structured knowledge, the methodology enhances LLMs’ reasoning abilities, enabling more accurate and efficient handling of complex tasks. Integration with open APIs allows LLMs to access external services and real-time data, expanding their functionality and application range. Through real-world case studies, we demonstrate that this approach significantly improves the efficiency and adaptability of LLM-based applications, especially for time-sensitive tasks. Our methodology provides practical guidelines for developers to rapidly create robust and adaptable LLM applications capable of navigating dynamic information environments and performing effectively across diverse tasks.

A Dynamic Knowledge Base Updating Mechanism-Based Retrieval-Augmented Generation Framework for Intelligent Question-and-Answer Systems

In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

Integrating Knowledge－Based Simulation with Aspiration－Directed Model－based Decision Support System

This paper reports an aspiration-directed, model-based decision support system (AMDSS) integrated with a knowledge-based simulation system. The system is designed to study China's mid-range economic development strategy. The capacity of the system is enhanced by the knowledge-based component which provides a knowledge-based simulation environment for model management. Currently the system has passed the stage of prototype and achieves its implementation capacity. The paper first presents the mathematical aspects of decision making including aspiration-directed decision making, then discusses the architecture of the system. The purpose of the paper is to provide insights into how such an integrated system could provide decision support for complex decision analysis.

Crossing the Growth Threshold： Service-Based Economy, Knowledge Process and Reshaping of Efficiency Model

The transition from middle-income to high-income stage is fraught with risks of growth divergence. Economic transition is clouded by the following possibilities： （1）falling share of industrial seetor through industrial depression and weakening growth momentum caused by the large urbanization costs; （2） the subordination of service sector as a result of nearly irreversibly industrial professional, which falters the process of service sector transition and upgrading,＂ （3） inefficient knowledge production allocation and human capital upgrade due to the absence of incentivized compensation of knowledge consumption. We suggest that a country should reshape its efficiency model by upgrading knowledge factor and human capital as the pre-requisite. Given the dilemmas of transition, China should take the faetorization trend of service sector and reshape efficiency model through institutional reform, ensuring that service sector will develop in tandem with industrial sector.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

A Cognitive Science Framework for the Analysis of Knowledge-Based Systems

The paper presents a cognitive science framework for the analysis of knowledge-based systems,including people, media. simulation and expert systems, resulting in a practical model for the procedures ofknowledge engineering. Starting with the construct of a social organization model driven by anticipationand thed differentiating this into pesonal scientists with diverse relations to people and their internal andexternal communication, it provides powerful and general model of society. people, and the roles of peoplein society. This model extends naturally ic the role of conventional media in the knowledge processes ofsociety and the new roles of computer-based simulation and expert systems. In particular it provides amodel of knowledge transfer that enables the processes of knowledge engineering to be analyzed andautomated.

Enhancing Domain Knowledge with Semantic Models of Web Documents

The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated with learnt classification rules categorizing documents into topics. Classification provides for the reduction of the dimensio0ality of the document feature space. The semantic model of retrieved web documents is semantically labeled by querying domain ontology and processed with content-based classification method. The model obtained is mapped to the existing knowledge base by implementing inference algorithm. It enables models of the same semantic type to be recognized and integrated into the knowledge base. The approach provides for the domain knowledge integration and assists the extraction and modeling web documents semantics. Implementation results of the proposed approach are presented.

Knowledge Formalization about Perforated Stomach Ulcer on the Basis of Medical Ontology

The article defines knowledge about pelforated stomach ulcer that is formalized on the basis of an ontology model of medical diagnostics domain. The paper describes a base of observations for the disease and also knowledge base which determines a clinical presentation of the disease. The dependences on courses of the disease and process localization are taken into account during knowledge formalizing. The base of knowledge for the disease has the structure that is conventional for contemporary medicine. These knowledge will be used for building a medical intellectual system of for consulting and diagnostics.

Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.CONCLUSION BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.

Integration of a Resource-Oriented Vocabulary with Knowledge-Oriented Vocabulary Systems

万维网信息网关面临着三大挑战:描述内容的直观的词汇表,词汇表系统的逻辑结构,以及不同的词汇表结构之间丰富的相互关系。本文试图解决美国国家教育图书馆发起的教育资源网关(GEM)遭遇的这些挑战。GEM的面向资源的词汇表定义了教育资源的范围和子类;然而,它缺乏主题类目之间和主题类目与关键词之间的相互关系。对GEM用户所作的一次调查表明,这种语义关联的缺乏对系统的检索效果具有负面的影响。作为对比,许多面向知识的词汇表系统含有语义关联及表达知识的结构。这篇论文报告了GEM语义项目第一阶段的成果,在这一阶段,作者通过分析其结构和特点,对GEM的受控词汇表增加了语义映射。在语义映射实验基础上,提出了两种模型来整合面向资源和知识的词汇表系统。元素-属性-值(EAV)模型注重于资源类型,可以方便地用文献类型定义来表达。语义层级模型则基于主题词条的语义含义和关系来对其加以处理。这两种整合模型可被用作词汇表建立和维护的理论框架。

Model Analytics辅助的智能放疗计划建模

目的:利用瓦里安公司开发的Model Analytics(MA)工具减少人工处理RapidPlan模型离群值的繁琐和主观因素导致模型构成的不确定性,评估MA工具在效率、改善统计学参数及模型优化效果等方面的表现。方法:(1)选取81例优质计划导入RapidPlan系统并建立初始模型;(2)将初始模型上传MA进行自动分析统计,根据报告提示对离群值进行批量统计学确认,比较模型验证前后统计学指标的变化;(3)利用20例测试病例评估统计学确认前后Rapid Plan模型的剂量学表现,并与原临床计划比较。结果:MA只需几分钟便可得到构成模型计划的几何学、剂量学等特征统计,5轮分析共找出8个股骨头剂量学离群值,分别高于各自预测范围上限的11.11%、5.88%、5.56%、5.56%、5.00%、5.26%、5.56%和5.88%,R^2由0.32提高至0.45;仅用一轮分析便找出所有3个膀胱几何和剂量学离群值,其中几何离群值分别高于均值62.22%或低于均值55.35%,剂量学离群值高于预测范围上限3.33%,处理完离群值后,R^2由0.35升至0.37。测试计划表明,Rapid Plan计划质量显著优于人工计划(P<0.05),使用验证前后的模型可分别降低股骨头剂量23.15%和27.55%,降低膀胱剂量8.14%和6.79%。结论:使用MA工具可快速获取模型构成计划的整体描述,并准确查找出模型中的离群值,从而提高智能放疗计划建模的效率,但统计学确认对模型的剂量学表现影响不大。

社区中老年慢性病患者个体化健康教育干预效果:一项整群随机对照试验

背景慢性病的频发与患者对健康知识及疾病风险认识不足有关,从全国范围来看,较为传统的健康教育方式依然广泛存在于社区卫生服务中,基层医生提供健康教育的形式单一、内容缺乏针对性,居民参与健康教育积极性普遍不足,对慢性病患者实施健康教育质量及效果有待提升。目的探究基于应用信息化知识库模型生成的个体化健康教育干预对社区中老年慢性病群体的影响,为进一步强化社区慢性病治理效果提供参考。方法于2021年选取北京市东城区社区卫生服务中心7390例患有4种慢性病(高血压、糖尿病、冠心病、脑卒中)的50~70岁患者作为研究对象,并进行为期1年的整群随机对照试验。对照组患者采用常规慢性病随访管理策略(保持原有的慢性病基本公共卫生服务项目);干预组患者在常规慢性病随访管理策略的基础上,应用信息化知识库模型生成健康教育指导方案的方式,即添加了健康教育处方指导和个体化健康管理的方式进行随访,每3个月进行1次随访及干预,共持续12个月。在所有患者入组1年后进行终线调查。本研究主要从“慢性病知识知晓率、自我管理态度、自我效能、服药依从性、健康信息化接受程度”等方面来分析两组慢性病患者在基线与终线调查之间数据结果的差异。结果共纳入7390例4种慢性病患者,其中干预组患者3673例,对照组患者3717例。两组慢性病患者年龄分布、性别、文化程度、工作状态比较,差异无统计学意义(P>0.05);两组慢性病患者医疗保障形式比较,差异有统计学意义(P<0.05)。干预组干预后整体疾病知识、慢性病基础知识、糖尿病知识、冠心病知识、脑卒中知识知晓正确率高于组内干预前(P<0.05);干预前后高血压知识知晓正确率比较,差异无统计学意义(P>0.05)。对照组患者干预后整体疾病知识、慢性病基础知识、高血压知识、糖尿病知识、冠心病知识知晓正确率与干预前比较,差异无统计学意义(P>0.05),脑卒中知识知晓正确率低于组内干预前(P<0.05)。干预组干预后自我管理态度问卷、自我效能问卷、服药依从性问卷、健康信息化接受度问卷得分均高于对照组(P<0.05)。干预后干预组自我管理态度问卷、自我效能问卷、服药依从性问卷、健康信息化接受度问卷得分均高于组内干预前(P<0.05)。对照组干预后自我效能问卷、服药依从性问卷得分高于组内干预前(P<0.05);对照组干预后自我管理态度问卷、健康信息化接受度问卷得分与组内干预前比较,差异无统计学意义(P>0.05)。结论从患者对慢性病知识知晓率、自我管理态度、信息化接受度方面可看出干预组患者改善效果明显优于对照组;从患者自我效能与服药依从性角度方面,两组患者在干预后均有提升,干预组效果更为显著。综合研究结果表明,通过信息化知识库模型进行个体化健康教育方式有助于慢性病患者健康素养提升。

A model-based analysis for mobile knowledge management in organizations

The consumerization of information technology(IT)has created a growing population of mobile knowledge workers that requires organizations to implement appropriate strategies of mobile knowledge management to support their businessrelated activities.This research presents and investigates an analytical model of mobile knowledge management by capturing the mutual effects of a central knowledge base and individual mobile devices for a mobile platform.Weinvestigate the best technology level of the mobile platform for both the homogeneous and heterogeneous settings and explore how they change with several critical factors including the central knowledge base,environmental mobility and knowledge volatility.Our model-based framework and analytical results provide valuable insights for practitioners to effectively design mobile platforms and manage mobile knowledge assets.

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.

An Intelligent Master Model of Computer Aided Process Planning for Large Complicated Stampings

Process planning for large complicated stampings is more complicated, illegible and multiform than that for common stampings. In this paper, an intelligent master model of computer aided process planning （CAPP） for large complicated stampings has been developed based on knowledge based engineering （KBE） and feature technology. This innovative model consists of knowledge base （KB）, process control structure （PCS）, process information model （PIM）, multidisciplinary design optimization （MDO）, model link environment （MLE） and simulation engine （SE）, to realize process planning, optimization, simulation and management integrated to complete intelligent CAPP system. In this model, KBE provides knowledge base, open architecture and knowledge reuse ability to deal with the multi-domain and multi-expression of process knowledge, and forms an integrated environment. With PIM, all the knowledge consisting of objects, constraints, cxtmricncc and decision-makings is carried by object-oriented method dynamically for knowledge-reasoning. PCS makes dynamical knowledge modified and updated timely and accordingly. MLE provides scv. cral methods to make CAPP sysmm associated and integrated. SE provides a programmable mechanism to interpret simulation course and result. Meanwhile, collaborative optimization, one method of MDO, is imported to deal with the optimization distributed for multiple purposes. All these make CAPP sysmm integrated and open to other systems, such as dic design and manufacturing system.

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.