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Multi-View Picture Fuzzy Clustering:A Novel Method for Partitioning Multi-View Relational Data 认领 引用 被引量：4
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作者 Pham Huy Thong Hoang Thi Canh +2 位作者 Luong Thi Hong Lan Nguyen Tuan Huy Nguyen Long Giang 《Computers, Materials & Continua》 SCIE EI 2025年第6期5461-5485,共25页
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl... Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications. 展开更多
关键词 Multi-view clustering picture fuzzy sets dual anchor graph fuzzy clustering multi-view relational data
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Support vector machine regression(SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization(RRN) 认领 引用 被引量：3
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作者 Jing Geng Wenxia Gan +2 位作者 Jinying Xu Ruqin Yang Shuliang Wang 《Geo-Spatial Information Science》 SCIE EI CSCD 2020年第3期237-247,I0004,共11页
Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating ... Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance. 展开更多
关键词 Support Vector machine Regression(SVR) non-linear radiometric transforming relation Relative Radiometric Normalization(RRN) multi-source data
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Semantic-based query processing for relational data integration 认领 引用 被引量：1
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作者 苗壮 张亚非 +2 位作者 王进鹏 陆建江 周波 《Journal of Southeast University(English Edition)》 EI CAS 2011年第1期22-25,共4页
To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,al... To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance. 展开更多
关键词 data integration relational database simple protocol and RDF query language（SPARQL） minimal connectable unit query processing
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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations 认领 引用
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作者 Fang Xu Songhao Jiang +3 位作者 Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu 《Computers, Materials & Continua》 SCIE EI 2024年第1期1095-1113,共19页
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social ... Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context. 展开更多
关键词 SIoT data forwarding social attributes social relations community
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Exploring relations between city regions based on mobile phone data 认领 引用
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作者 汪烁枫 李志恒 +1 位作者 姜山 谢娜 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第7期1799-1806,共8页
City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations b... City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations between regions of Shanghai based on mobile phone data is explored. By formulating the regions as nodes in a network and the commuting between each pair of regions as link weights, the distribution of nodes degree, and spatial structures of communities in this relation network are studied. Statistics show that regions locate in urban centers and traffic hubs have significantly larger degrees. Moreover, two kinds of spatial structures of communities are found. In most communities, nodes are spatially neighboring. However, in the communities that cover traffic hubs, nodes often locate along corridors. 展开更多
关键词 mobile phone data city relations community degree
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Spatio-temporal Data Model Based on Relational Database System 认领 引用
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作者 SHA Zongyao BIAN Fuling 《Geo-Spatial Information Science》 EI 2002年第2期22-27,共6页
In this paper,the entity_relation data model for integrating spatio_temporal data is designed.In the design,spatio_temporal data can be effectively stored and spatiao_temporal analysis can be easily realized.
关键词 GIS spatio_temporal data model relational database spatio_temporal analysis
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Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model 认领 引用 被引量：6
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作者 Jing-chun Feng Hua-ai Huang +1 位作者 Yao Yin Ke Zhang 《Water Science and Engineering》 EI CAS CSCD 2019年第4期330-338,共9页
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ... Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data. 展开更多
关键词 Security risk factor identification Heterogeneous data Grey relational analysis model Relational degree Information entropy Conditional entropy Small reservoir Guangxi
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Dynamic Relative Advantage-Driven Multi-Fault Synergistic Diagnosis Method for Motors under Imbalanced Missing Data Rates 认领 引用
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作者 Zhenpeng Teng Xiaojian Yi Biao Wang 《Journal of Dynamics, Monitoring and Diagnostics》 2025年第2期111-120,共10页
Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.T... Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.These studies,however,have the following limitations:1)effective supervision is neglected for missing data across different fault types and 2)imbalance in missing rates among fault types results in inadequate learning during model training.To overcome the above limitations,this paper proposes a dynamic relative advantagedriven multi-fault synergistic diagnosis method to accomplish accurate fault diagnosis of motors under imbalanced missing data rates.Firstly,a cross-fault-type generalized synergistic diagnostic strategy is established based on variational information bottleneck theory,which is able to ensure sufficient supervision in handling missing data.Then,a dynamic relative advantage assessment technique is designed to reduce diagnostic accuracy decay caused by imbalanced missing data rates.The proposed method is validated using multi-sensor data from motor fault simulation experiments,and experimental results demonstrate its effectiveness and superiority in improving diagnostic accuracy and generalization under imbalanced missing data rates. 展开更多
关键词 data missing motor fault relative advantage synergistic diagnosis
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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases 认领 引用
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作者 Norwini Zaidi Iskandar Ishak +1 位作者 Fatimah Sidi Lilly Suriani Affendey 《Computer Systems Science & Engineering》 SCIE EI 2022年第12期1175-1188,共14页
Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational... Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase. 展开更多
关键词 Data migration data transformation column-oriented database relational database big data
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Development of Granular Fuzzy Relation Equations Based on a Subset of Data 认领 引用
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作者 Dan Wang Xiubin Zhu +2 位作者 Witold Pedycz Zhenhua Yu Zhiwu Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第8期1416-1427,共12页
Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated t... Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm. 展开更多
关键词 A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization(PSO)
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On the Relation Redundancy in Fuzzy Databases 认领 引用
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作者 唐晓辉 陈国青 《Journal of Donghua University(English Edition)》 EI CAS 2006年第6期67-70,83,共4页
This paper concentrates on the problem of data redundancy under the extended-possibility-based model. Based on the information gain in data classification, a measure - relation redundancy - is proposed to evaluate the... This paper concentrates on the problem of data redundancy under the extended-possibility-based model. Based on the information gain in data classification, a measure - relation redundancy - is proposed to evaluate the degree of a given relation being redundant in whole. The properties of relation redundancy are also investigated. This new measure is useful in dealing with data redundancy. 展开更多
关键词 fuzzy relational data model closeness relation redundancy.
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A Distribution Management System for Relational Databases in Cloud Environments 认领 引用
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作者 Sze-Yao Li Chun-Ming Chang +3 位作者 Yuan-Yu Tsai Seth Chen Jonathan Tsai Wen-Lung Tsai 《Journal of Electronic Science and Technology》 CAS 2013年第2期169-175,共7页
For a transaction processing system to operate effectively and efficiently in cloud environments, it is important to distribute huge amount of data while guaranteeing the ACID （atomic, consistent, isolated, and dura... For a transaction processing system to operate effectively and efficiently in cloud environments, it is important to distribute huge amount of data while guaranteeing the ACID （atomic, consistent, isolated, and durable） properties. Moreover, database partition and migration tools can help transplanting conventional relational database systems to the cloud environment rather than rebuilding a new system. This paper proposes a database distribution management （DBDM） system, which partitions or replicates the data according to the transaction behaviors of the application system. The principle strategy of DBDM is to keep together the data used in a single transaction, and thus, avoiding massive transmission of records in join operations. The proposed system has been implemented successfully. The preliminary experiments show that the DBDM performs the database partition and migration effectively. Also, the DBDM system is modularly designed to adapt to different database management system （DBMS） or different partition algorithms. 展开更多
关键词 Data migration database partition distributed database relational database.
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A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation 认领 引用
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作者 CHEN Geng NI Wei-wei +1 位作者 ZHU Yu-quan SUN Zhi-hui 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第1期27-30,共4页
Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only ... Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as ＂and＂, ＂or＂ and ＂xor＂. Applying this idea in the existed distributed association rule mining al gorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient. 展开更多
关键词 frequent itemsets distributed association rule mining relation of itemsets-binary data
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Developing a Data Modelling Tool to Visualize the Transformation of an ER Diagram into a Relational Schema 认领 引用
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作者 Elitsa Silyanova Arsova Silyan Sibinov Arsov 《Computer Technology and Application》 2012年第3期201-205,共5页
In this paper, the authors present the development of a data modelling tool that visualizes the transformation process of an ＂Entity-Relationship＂ Diagram （ERD） into a relational database schema. The authors＇ foc... In this paper, the authors present the development of a data modelling tool that visualizes the transformation process of an ＂Entity-Relationship＂ Diagram （ERD） into a relational database schema. The authors＇ focus is the design of a tool for educational purposes and its implementation on e-learning database course. The tool presents two stages of database design. The first stage is to draw ERD graphically and validate it. The drawing is done by a learner. Then at second stage, the system enables automatically transformation of ERD to relational database schema by using common rules. Thus, the learner could understand more easily how to apply the theoretical material. A detailed description of system functionalities and algorithm for the conversion are proposed. Finally, a user interface and usage aspects are exposed. 展开更多
关键词 E-learning course data modelling tool entity-relationship model relational model.
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Research of storage technique for C-Temporal Relation Data Model 认领 引用
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作者 WANG Zhi-wen CAI Qi-xian Wang Hu-Qi 《微计算机信息》 北大核心 2008年第9期273-275,共3页
As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly.We put forward compress storage tactics for temporal datum which com... As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly.We put forward compress storage tactics for temporal datum which combine compress technology in existence in order to settle datum redundancy in the course of temporal datum storage and temporal datum of slow acting domain and momentary acting domain are accessed by using each from independence clock method and mutual clock method.We also bring forward strategy of gridding storage to resolve the problems of temporal datum rising rapidly. 展开更多
关键词 Temporal Relation Data Model Datum redundancy Compress storage Independence clock method Mutual clock method Gridding Storage
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GraphCon:A Parallel Graph Construction from Relational Data 认领 引用
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作者 Bowen Dong Wenjun Wang +2 位作者 Xueli Liu Yuejun Wang Dan Yin 《Big Data Mining and Analytics》 EI CSCD 2026年第2期448-464,共17页
Converting relational data into a property graph is advantageous for relational data analysis using graph algorithms.However,existing methods for constructing property graphs from relational data often require complex... Converting relational data into a property graph is advantageous for relational data analysis using graph algorithms.However,existing methods for constructing property graphs from relational data often require complex join operations when predefined entities and relationships are given.Additionally,constructing graphs from large-scale relational data is time-consuming due to the need to aggregate instances from multiple tables.To address this issue,this paper proposes a schema-based graph construction method called GraphCon.GraphCon employs a schema-based mapping mechanism to achieve equivalent mapping between the graph schema and the relational schema.Additionally,we optimize a complex join strategy,InstanceJoin,in the graph construction process.To improve efficiency in handling large-scale data,we introduce a parallel algorithm that includes a data partition strategy based on the graph schema and a load-balancing strategy to enhance scalability.Experiments using the TPC-H benchmark and real-life datasets validate the efficiency and scalability of our proposed methods. 展开更多
关键词 relational data transformation data integration graph construction parallel scalability
Damage Identification in Beam-Type Structures Using Pseudo Strain Energy Density and Grey Relation Coefficient 认领 引用
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作者 陈晓强 朱宏平 +1 位作者 张俊兵 李林 《Transactions of Tianjin University》 EI CAS 2010年第2期96-103,共8页
Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify th... Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify the structural damage severity of confirmed damaged locations. Furthermore, a systematic damage identification program based on GA is developed on MATLAB platform. ANSYS is employed to conduct the finite element analysis of complicated civil engineering structures, which is embedded with interface technique. The two-step damage identification is verified by a finite element model of Xinxingtang Highway Bridge and a laboratory beam model based on polyvinylidens fluoride (PVDF). The bridge model was constructed with 57 girder segments, and simulated with 58 measurement points. The damaged segments were located accurately by GRC index regardless of damage extents and noise levels. With stiffness reduction factors of detected segments as variables, the GA program evolved for 150 generations in 6 h and identified the damage extent with the maximum errors of 1% and 3% corresponding to the noise to signal ratios of 0 and 5%, respectively. In contrast, the common GA-based method without using GRC index evolved for 600 generations in 24 h, but failed to obtain satisfactory results. In the laboratory test, PVDF patches were used as dynamic strain sensors, and the damage locations were identified due to the fact that GRC indexes of points near damaged elements were smaller than 0.6 while those of others were larger than 0.6. The GA-based damage quantification was also consistent with the value of crack depth in the beam model. 展开更多
关键词 damage identification strain test data pseudo strain energy density grey relation coefficient genetic algorithm polyvinylidens fluoride
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PORLES:A Parallel Object Relational Database System 认领 引用
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作者 Sun Yong\ting, Zhu Feng\huaDepartment of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030,China 《Wuhan University Journal of Natural Sciences》 EI CAS 2001年第Z1期100-109,共10页
We developed a parallel object relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel quer... We developed a parallel object relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel query optimization, transaction processing system and parallel access method in detail. 展开更多
关键词 parallel object relational database BSP model data model query optimization
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A Two-Phase Paradigm for Joint Entity-Relation Extraction 认领 引用 被引量：2
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作者 Bin Ji Hao Xu +4 位作者 Jie Yu Shasha Li JunMa Yuke Ji Huijun Liu 《Computers, Materials & Continua》 SCIE EI 2023年第1期1303-1318,共16页
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However,these models sample a large number of negative entities and negative relations during t... An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However,these models sample a large number of negative entities and negative relations during the model training,which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance.In order to address the above issues,we propose a two-phase paradigm for the span-based joint entity and relation extraction,which involves classifying the entities and relations in the first phase,and predicting the types of these entities and relations in the second phase.The two-phase paradigm enables our model to significantly reduce the data distribution gap,including the gap between negative entities and other entities,aswell as the gap between negative relations and other relations.In addition,we make the first attempt at combining entity type and entity distance as global features,which has proven effective,especially for the relation extraction.Experimental results on several datasets demonstrate that the span-based joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-ofthe-art span-based models for the joint extraction task,establishing a new standard benchmark.Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features. 展开更多
关键词 Joint extraction span-based named entity recognition relation extraction data distribution global features
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Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models 认领 引用 被引量：1
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作者 Jiakai Li Jianpeng Hu Geng Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第5期2481-2503,共23页
In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple e... In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach. 展开更多
关键词 Relational triple extraction semantic interaction large language models data augmentation specific domains
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