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Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath
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作者 Shiming Lin Ling Ye +4 位作者 Yijie Zhuang Lingyun Lu Shaoqiu Zheng Chenxi Huang Ng Yin Kwee 《Computers, Materials & Continua》 SCIE EI 2024年第7期299-317,共19页
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 mi... 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. 展开更多
关键词 Intelligent agent knowledge graph reasoning REINFORCEMENT transfer learning
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GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning
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作者 Yu Song Pengcheng Wu +2 位作者 Dongming Dai Mingyu Gui Kunli Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第9期4767-4790,共24页
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 me... 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. 展开更多
关键词 Intelligent diagnosis knowledge graph graph attention network knowledge reasoning
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IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations
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作者 Yajing Ma Gulila Altenbek Yingxia Yu 《Computers, Materials & Continua》 SCIE EI 2024年第1期695-712,共18页
Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurr... Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness. 展开更多
关键词 Knowledge reasoning entity and relation representation structural dependency relationship evolutionary representation temporal graph convolution
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Challenges Experienced by Nurse Educators in Promoting Acquisition of Clinical Reasoning Skills by the Undergraduate Nursing Students: A Qualitative Exploratory Study
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作者 Omero. G. Mwale Mukwato-Katowa Patricia Marjorie Kabinga-Makukula 《Open Journal of Nursing》 2024年第8期459-476,共18页
Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is... Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is difficult because of its complexity. This study, therefore aimed at exploring the challenges experienced by nurse educators in promoting acquisition of clinical reasoning skills by undergraduate nursing students. Methods: A qualitative exploratory research design was used in this study. The participants were purposively sampled and recruited into the study. Data were collected using semi-structured interview guides. Thematic analysis method was used to analyze the collected data The principles of beneficence, respect of human dignity and justice were observed. Results: The findings have shown that clinical learning environment, lacked material and human resources. The students had no interest to learn the skill. There was also knowledge gap between nurse educators and clinical nurses. Lack of role model was also an issue and limited time exposure. Conclusion: The study revealed that nurse educators encounter various challenges in promoting the acquisition of clinical reasoning skills among undergraduate nursing students. Training institutions and hospitals should periodically revise the curriculum and provide sufficient resources to facilitate effective teaching and learning of clinical reasoning. Nurse educators must also update their knowledge and skills through continuous professional development if they are to transfer the skill effectively. 展开更多
关键词 ACQUISITION Clinical reasoning Skills Undergraduate Nursing Student Nurse Educator
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Assessing the Levels of Clinical Reasoning Skills Using Self-Assessment of Clinical Reflection and Reasoning in Undergraduate Nursing Students: A Descriptive Comparative Study
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作者 Omero G. Mwale Patricia K. Mukwato Marjorie K. Makukula‡ 《Open Journal of Nursing》 2024年第7期283-297,共15页
Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The... Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The aim of the study was to assess and compare the levels of clinical reasoning skills between third year and fourth year undergraduate nursing students. Methods: The study utilized a descriptive comparative research design, based on the positivism paradigm. 410 undergraduate nursing students were systematically sampled and recruited into the study. The researchers used the Self-Assessment of Clinical Reflection and Reasoning questionnaire to collect data on clinical reasoning skills from third- and fourth-year nursing students while adhering to ethical principles of human dignity. Descriptive statistics were done to analyse the level of clinical reasoning and an independent sample t-test was performed to compare the clinical reasoning skills of the student. A p value of 0.05 was accepted. Results: The results of the study revealed that the mean clinical reasoning scores of the undergraduate nursing students were knowledge/theory application (M = 3.84;SD = 1.04);decision-making based on experience and evidence (M = 4.09;SD = 1.01);dealing with uncertainty (M = 3.93;SD = 0.87);reflection and reasoning (M = 3.77;SD = 3.88). The mean difference in clinical reasoning skills between third- and fourth-year undergraduate nursing students was not significantly different from an independent sample t-test scores (t = −1.08;p = 0.28);(t = −0.29;p = 0.73);(t = 1.19;p = 0.24);(t = −0.57;p = 0.57). Since the p-value is >0.05, the null hypothesis (H0) “there is no significantno significant difference in clinical reasoning between third year and fourth year undergraduate nursing students”, was accepted. Conclusion: This study has shown that the level of clinical reasoning skills of the undergraduate nursing students was moderate to low. This meant that the teaching methods have not been effective to improve the students clinical reasoning skills. Therefore, the training institutions should revise their curriculum by incorporating new teaching methods like simulation to enhance students’ clinical reasoning skills. In conclusion, evaluating clinical reasoning skills is crucial for addressing healthcare issues, validating teaching methods, and fostering continuous improvement in nursing education. 展开更多
关键词 Clinical Competences Clinical reasoning Skills Undergraduate Nursing Student
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Local-to-Global Causal Reasoning for Cross-Document Relation Extraction
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作者 Haoran Wu Xiuyi Chen +3 位作者 Zefa Hu Jing Shi Shuang Xu Bo Xu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第7期1608-1621,共14页
Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing nois... Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts.Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity.However,similarity-based methods cannot distinguish valid information from highly similar retrieved documents well.How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue.To address this problem,we design a novel local-toglobal causal reasoning(LGCR)network for cross-document RE,which enables efficient distinguishing,filtering and global reasoning on complex information from a causal perspective.Specifically,we propose a local causal estimation algorithm to estimate the causal effect,which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE.Furthermore,based on the causal effect,we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning.Experimental results under the closed and the open settings of the large-scale dataset Cod RED demonstrate our LGCR network significantly outperforms the state-ofthe-art methods and validate the effectiveness of causal reasoning in confusing information processing. 展开更多
关键词 Causal reasoning cross document graph reasoning relation extraction(RE)
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Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning 被引量:1
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作者 Zibo Wang Chaobin Huo +5 位作者 Yaofang Zhang Shengtao Cheng Yilu Chen Xiaojie Wei Chao Li Bailing Wang 《Computers, Materials & Continua》 SCIE EI 2023年第5期2957-2979,共23页
With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecas... With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy. 展开更多
关键词 Path-based reasoning representation learning attention mechanism vulnerability knowledge graph industrial control component
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Intelligent Color Reasoning of IOT Based on P-laws
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作者 HuangJing Yu Jinming Qiu +1 位作者 Ning Cao Russell Higgs 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期3181-3193,共13页
Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoret... Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission.Based on the HSL(Hue,Saturation,Lightness)color solid,the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System(DCS),establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid.The experimental results show that based on the color P-law,for the DCS of the natural environment,when the external factors change,the color of the plant changes,accordingly,verifying the effectiveness of the color P-law generation model and the algorithm of the DCS.In the dynamic color intel-ligent simulation system,when external factors change,the dynamic change of plant color generally conforms to the basic laws of the natural environment.This enables the effective extraction of color data from the Internet of Things(IoT)-based color sensors and provides an effective way to significantly reduce the data transmission bandwidth of the IoT network. 展开更多
关键词 Natural environment function P-sets color P-law intelligent color reasoning simulation
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An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM
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作者 N.Vanitha C.R.Rene Robin D.Doreen Hephzibah Miriam 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2323-2336,共14页
Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather fo... Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates. 展开更多
关键词 Tropical cyclones classification support vector machine ontology SWRL reasoning SVM classification
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A case-based reasoning method of recognizing liquefaction pits induced by 2021 M_(W) 7.3 Madoi earthquake
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作者 Peng Liang Yueren Xu +2 位作者 Wenqiao Li Yanbo Zhang Qinjian Tian 《Earthquake Research Advances》 CSCD 2023年第1期61-69,共9页
Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits base... Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits based on high-resolution satellite images.Therefore,we provide a framework for extracting liquefaction pits based on a case-based reasoning method.Furthermore,five covariates selection methods were used to filter the 11 covariates that were generated from high-resolution satellite images and digital elevation models(DEM).The proposed method was trained with 450 typical samples which were collected based on visual interpretation,then used the trained case-based reasoning method to identify the liquefaction pits in the whole study area.The performance of the proposed methods was evaluated from three aspects,the prediction accuracies of liquefaction pits based on the validation samples by kappa index,the comparison between the pre-and post-earthquake images,the rationality of spatial distribution of liquefaction pits.The final result shows the importance of covariates ranked by different methods could be different.However,the most important of covariates is consistent.When selecting five most important covariates,the value of kappa index could be about 96%.There also exist clear differences between the pre-and post-earthquake areas that were identified as liquefaction pits.The predicted spatial distribution of liquefaction is also consistent with the formation principle of liquefaction. 展开更多
关键词 Coseismic liquefaction Case-based reasoning K-nearest neighbor Covariates selection 2021 M_(w)7.3 Madoi earthquake Qinghai-Tibetan Plateau
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Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach
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作者 彭欣 姚帅寓 +1 位作者 胡昊 杜守继 《Journal of Donghua University(English Edition)》 CAS 2023年第2期202-215,共14页
Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessm... Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs. 展开更多
关键词 underground utility tunnel(UUT) risk assessment evidential reasoning(ER) operation and maintenance(O&M)
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A reasoning diagram based method for fault diagnosis of railway point system
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作者 Feng Wang Yuan Cao +4 位作者 Clive Roberts Tao Wen Lei Tan Shuai Su Tao Tang 《High-Speed Railway》 2023年第2期110-119,共10页
Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing met... Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs. 展开更多
关键词 Railway point system Fault diagnosis reasoning diagram SEGMENTATION Detection method
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Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics
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作者 Zhaohao Sun 《Journal of Computer Science Research》 2023年第3期1-14,共14页
Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process ... Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning. 展开更多
关键词 Similarity intelligence Similarity computing Similarity analytics Similarity-based reasoning Big data analytics Artificial intelligence Intelligent agents
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REPRESENTATION PROPERTIES OF ABSTRACT DEFAULT REASONING FRAMEWORKS
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作者 曹子宁 毛宇光 石纯一 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第3期214-221,共8页
presented The conceptions of abstract default reasoning frameworks (ADRFs) and D-consequence relations are Based on representation properties of D-consequence relations, it proves that any cumulative nonmonotonic co... presented The conceptions of abstract default reasoning frameworks (ADRFs) and D-consequence relations are Based on representation properties of D-consequence relations, it proves that any cumulative nonmonotonic consequence relation with the connective-free form can be represented by ADRFs. 展开更多
关键词 abstract default reasoning framework representation property nonmonotonie reasoning
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CAPP SYSTEM BASED ON WEB AND SUCCESSIVE CASE REASONING 被引量:1
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作者 黄翔 方挺立 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2005年第3期240-246,共7页
Aiming at practical demands of manufacturing enterprises to the CAPP system in the Internet age, the CAPP model is presented based on Web and featured by open, universality and intelligence. A CAPP software package is... Aiming at practical demands of manufacturing enterprises to the CAPP system in the Internet age, the CAPP model is presented based on Web and featured by open, universality and intelligence. A CAPP software package is developed with three layer structures (the database, the Web server and the client server) to realize CAPP online services. In the CAPP software package, a new process planning method called the successive casebased reasoning is presented. Using the method, process planning procedures are divided into three layers (the process planning, the process procedure and the process step), which are treated with the successive process reasoning. Process planning rules can be regularly described due to the granularity-based rule classification. The CAPP software package combines CAPP software with online services. The process planning has the features of variant analogy and generative creation due to adopting the successive case-based reasoning, thus improving the universality and the practicability of the process planning. 展开更多
关键词 CAPP case-based reasoning online services
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New reasoning algorithm based on EFALC
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作者 周波 陆建江 +2 位作者 张亚非 康达周 李言辉 《Journal of Southeast University(English Edition)》 EI CAS 2006年第4期496-500,共5页
The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reas... The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reasoning algorithm is given. This algorithm is designed in the style of tableau algorithms, which is usually used in classical description logics. The transformation rules and the process of this algorithm is described and optimized with three main techniques: recursive procedure call, branch cutting and introducing sets of mesne results. The optimized algorithm is proved sound, complete and with an EXPTime complexity, and the satisfiability problem is EXPTime-complete. 展开更多
关键词 extended fuzzy description logic cut concept TBox reasoning algorithm
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Reasoning complexity for extended fuzzy description logic with qualifying number restriction
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作者 陆建江 李言辉 +2 位作者 张亚非 周波 康达周 《Journal of Southeast University(English Edition)》 EI CAS 2007年第2期236-240,共5页
To solve the extended fuzzy description logic with qualifying number restriction (EFALCQ) reasoning problems, EFALCQ is discretely simulated by description logic with qualifying number restriction (ALCQ), and ALCQ... To solve the extended fuzzy description logic with qualifying number restriction (EFALCQ) reasoning problems, EFALCQ is discretely simulated by description logic with qualifying number restriction (ALCQ), and ALCQ reasoning results are reused to prove the complexity of EFALCQ reasoning problems. The ALCQ simulation method for the consistency of EFALCQ is proposed. This method reduces EFALCQ satisfiability into EFALCQ consistency, and uses EFALCQ satisfiability to discretely simulate EFALCQ satdomain. It is proved that the reasoning complexity for EFALCQ satisfiability, consistency and sat-domain is PSPACE-complete. 展开更多
关键词 extended fuzzy description logic qualifying number restriction reasoning complexity
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Mathematical Foundation of Basic Algorithms of Fuzzy Reasoning 被引量:1
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作者 潘正华 《Journal of Shanghai University(English Edition)》 CAS 2005年第3期219-223,共5页
Algorithm of fuzzy reasoning has been successful applied in fuzzy control,but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper,structure of basic algorithms of fuzzy reasoni... Algorithm of fuzzy reasoning has been successful applied in fuzzy control,but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper,structure of basic algorithms of fuzzy reasoning was studied, its rationality was discussed from the viewpoint of logic and mathematics, and three theorems were proved. These theorems shows that there always exists a mathe-~matical relation (that is, a bounded real function) between the premises and the conclusion for fuzzy reasoning, and in fact various algorithms of fuzzy reasoning are specific forms of this function. Thus these results show that algorithms of fuzzy reasoning are theoretically reliable. 展开更多
关键词 fuzzy reasoning algorithm of fuzzy reasoning FMP (fuzzy modus ponens) CRI(compositional rule of inference) algorithm 3I algorithm.
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IMPROVED AERODYNAMIC APPROXIMATION MODEL WITH CASE-BASED REASONING TECHNIQUE FOR MDO OF AIRCRAFT
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作者 白振东 刘虎 +1 位作者 柴雪 武哲 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第3期187-193,共7页
To increase the efficiency of the multidisciplinary optimization of aircraft, an aerodynamic approximation model is improved. Based on the study of aerodynamic approximation model constructed by the scaling correction... To increase the efficiency of the multidisciplinary optimization of aircraft, an aerodynamic approximation model is improved. Based on the study of aerodynamic approximation model constructed by the scaling correction model, case-based reasoning technique is introduced to improve the approximation model for optimization. The aircraft case model is constructed by utilizing the plane parameters related to aerodynamic characteristics as attributes of cases, and the formula of case retrieving is improved. Finally, the aerodynamic approximation model for optimization is improved by reusing the correction factors of the most similar aircraft to the current one. The multidisciplinary optimization of a civil aircraft concept is carried out with the improved aerodynamic approximation model. The results demonstrate that the precision and the efficiency of the optimization can be improved by utilizing the improved aerodynamic approximation model with ease-based reasoning technique. 展开更多
关键词 AIRCRAFT aerodynamic approximation model case-based reasoning multidisciplinary optimization(MDO)
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Optimizing reasoning in EL^(++) ontologies by using boundary-based module
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作者 方俊 郭雷 杨宁 《Journal of Southeast University(English Edition)》 EI CAS 2009年第4期482-485,共4页
In order to optimize ontology reasoning, a novel boundary-based modular extraction method is introduced for ontologies in EL^++ description logics. The proposed module extraction method is capable of identifying rel... In order to optimize ontology reasoning, a novel boundary-based modular extraction method is introduced for ontologies in EL^++ description logics. The proposed module extraction method is capable of identifying relevant axioms in an ontology based on the notion of boundaries of symbols, with respect to a given reasoning task. Exactness of the method is ensured by discovering all axioms in the original ontology that may be directly or indirectly relevant to boundaries of symbols used in the reasoning task. Compactness of the method is ensured by boundary partition and intersection operation performed in the process of module extraction. The theoretical foundation and a practical algorithm for computing the proposed axiom-based modules are presented. The proposed algorithm is implemented for the description logic EL^++. Experimental results on realworld ontologies show that, based on the proposed modularization method, the performance of ontology reasoning can be significantly improved. 展开更多
关键词 BOUNDARY module extraction reasoning optimization axiom-based module
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