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Multi-Modal Pre-Synergistic Fusion Entity Alignment Based on Mutual Information Strategy Optimization 认领 引用
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作者 Huayu Li Xinxin Chen +3 位作者 Lizhuang Tan Konstantin I.Kostromitin Athanasios V.Vasilakos Peiying Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第11期4133-4153,共21页
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities... To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model. 展开更多
关键词 Knowledge graph multi-modal entity alignment feature fusion pre-synergistic fusion
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MMCSD:Multi-Modal Knowledge Graph Completion Based on Super-Resolution and Detailed Description Generation 认领 引用
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作者 Huansha Wang Ruiyang Huang +2 位作者 Qinrang Liu Shaomei Li Jianpeng Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第4期761-783,共23页
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and ... Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance. 展开更多
关键词 Multi-modal knowledge graph knowledge graph completion multi-modal fusion
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MDGET-MER:Multi-Level Dynamic Gating and Emotion Transfer for Multi-Modal Emotion Recognition 认领 引用
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作者 Musheng Chen Qiang Wen +2 位作者 Xiaohong Qiu Junhua Wu Wenqing Fu 《Computers, Materials & Continua》 SCIE EI 2026年第3期872-893,共22页
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method... In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets. 展开更多
关键词 Multi-modal emotion recognition dynamic gating emotion transfer module cross-modal dynamic alignment noise robustness
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KIG:A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation 认领 引用
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作者 Siyuan Xu Dong Pan +3 位作者 Zhaohui Jiang Zhiwen Chen Haoyang Yu Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期327-345,共19页
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider... Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS. 展开更多
关键词 Blast furnace ironmaking process graph neural network(GNN) knowledge graph(KG) multisensor time series(MTS) temporal alignment time-delay estimation(TDE)
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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs 认领 引用 被引量：11
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作者 Linyao Yang Chen Lv +4 位作者 Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第11期1990-2004,共15页
Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power system... Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs. 展开更多
关键词 Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch
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Multi-Modal Military Event Extraction Based on Knowledge Fusion 认领 引用
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作者 Yuyuan Xiang Yangli Jia +1 位作者 Xiangliang Zhang Zhenling Zhang 《Computers, Materials & Continua》 SCIE EI 2023年第10期97-114,共18页
Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event eleme... Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data.Although researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater depth.Furthermore,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion.The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the model.These results validate the effectiveness of our method in extracting event elements from multi-modal data. 展开更多
关键词 Event extraction multi-modal knowledge fusion pre-trained models
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Multi-modal knowledge graph inference via media convergence and logic rule 认领 引用 被引量：2
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作者 Feng Lin Dongmei Li +5 位作者 Wenbin Zhang Dongsheng Shi Yuanzhou Jiao Qianzhong Chen Yiying Lin Wentao Zhu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期211-221,共11页
Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the intro... Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features. 展开更多
关键词 logic rule media convergence multi-modal knowledge graph inference representation learning
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DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning 认领 引用
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作者 Leilei Li Yansheng Fu +6 位作者 Dongjie Zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs 《Intelligent Automation & Soft Computing》 SCIE 2023年第6期3295-3307,共13页
The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,... The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space. 展开更多
关键词 Multi-modal retrieval distributed storage knowledge graph
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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge 认领 引用 被引量：1
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作者 Yutao Chen Shumin Shi Heyan Huang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期375-386,共12页
Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common.However,detecting sarcasm in various forms of communication can be difficult due to confli... Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common.However,detecting sarcasm in various forms of communication can be difficult due to conflicting sentiments.In this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and image.Then,five external sentiments are introduced to prompt the model learning sentimental preferences among modalities.Furthermore,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike features.Experimental results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset. 展开更多
关键词 CLIP image-text classification knowledge fusion multi-modal sarcasm detection
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Large-scale Entity Alignment in Knowledge Graphs Using Language Models 认领 引用
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作者 Ningxin Chen Zhichun Wang 《Data Intelligence》 EI CSCD 2026年第1期137-163,共27页
Entity alignment(EA)is crucial for knowledge fusion and integration,as it aims to match equivalent entities across different KGs.Recently,many neural-based EA methods have been proposed,focusing on developing various ... Entity alignment(EA)is crucial for knowledge fusion and integration,as it aims to match equivalent entities across different KGs.Recently,many neural-based EA methods have been proposed,focusing on developing various graph representation learning models to match entities in vector spaces.However,most real-world KGs are large-scale and contain rich structural and attribute information about entities,presenting challenges for current approaches designed primarily for small-and medium-sized KGs.To address the challenges of large-scale EA,this paper introduces a simple,effective,and scalable method based on language models.Our approach first leverages the capabilities of language models to encode entities'multi-view information into low-dimensional embeddings,identifying potential aligned entity pairs with high similarity.These candidates are then re-ranked using a global matching algorithm to produce the final alignments.Experimental results show that our method achieves state-of-the-art performance on real-world large-scale EA datasets,with superior accuracy and efficiency compared to existing methods. 展开更多
关键词 Knowledge graph Pre-trained language model Entity alignment Large-scale entity alignment Dense retrieval
A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications 认领 引用 被引量：22
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作者 Tianzhe Jiao Chaopeng Guo +2 位作者 Xiaoyue Feng Yuming Chen Jie Song 《Computers, Materials & Continua》 SCIE EI 2024年第7期1-35,共35页
Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant resear... Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges. 展开更多
关键词 Multi-modal fusion representation translation alignment deep learning comparative analysis
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MMDistill:Multi-Modal BEV Distillation Framework for Multi-View 3D Object Detection 认领 引用 被引量：1
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作者 Tianzhe Jiao Yuming Chen +2 位作者 Zhe Zhang Chaopeng Guo Jie Song 《Computers, Materials & Continua》 SCIE EI 2024年第12期4307-4325,共19页
Multi-modal 3D object detection has achieved remarkable progress,but it is often limited in practical industrial production because of its high cost and low efficiency.The multi-view camera-based method provides a fea... Multi-modal 3D object detection has achieved remarkable progress,but it is often limited in practical industrial production because of its high cost and low efficiency.The multi-view camera-based method provides a feasible solution due to its low cost.However,camera data lacks geometric depth,and only using camera data to obtain high accuracy is challenging.This paper proposes a multi-modal Bird-Eye-View(BEV)distillation framework(MMDistill)to make a trade-off between them.MMDistill is a carefully crafted two-stage distillation framework based on teacher and student models for learning cross-modal knowledge and generating multi-modal features.It can improve the performance of unimodal detectors without introducing additional costs during inference.Specifically,our method can effectively solve the cross-gap caused by the heterogeneity between data.Furthermore,we further propose a Light Detection and Ranging(LiDAR)-guided geometric compensation module,which can assist the student model in obtaining effective geometric features and reduce the gap between different modalities.Our proposed method generally requires fewer computational resources and faster inference speed than traditional multi-modal models.This advancement enables multi-modal technology to be applied more widely in practical scenarios.Through experiments,we validate the effectiveness and superiority of MMDistill on the nuScenes dataset,achieving an improvement of 4.1%mean Average Precision(mAP)and 4.6%NuScenes Detection Score(NDS)over the baseline detector.In addition,we also present detailed ablation studies to validate our method. 展开更多
关键词 3D object detection multi-modal knowledge distillation deep learning remote sensing
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Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment 认领 引用
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作者 LV Zhaoming PENG Rong 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2022年第3期240-254,共15页
Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent adv... Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods. 展开更多
关键词 ontology alignment multi-objective grasshopper optimization algorithm ε-dominance fast non-dominated sorting knowledge integration
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KnowBench:Evaluating the Knowledge Alignment on Large Visual Language Models 认领 引用
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作者 Zheng Ma Hao-Tian Yang +1 位作者 Jian-Bing Zhang Jia-Jun Chen 《Journal of Computer Science & Technology》 SCIE EI CSCD 2025年第5期1209-1219,共11页
Large visual language models(LVLMs)have revolutionized the multimodal domain,demonstrating exceptional performance in tasks requiring fusing visual and textual information.However,the current evaluation benchmarks fai... Large visual language models(LVLMs)have revolutionized the multimodal domain,demonstrating exceptional performance in tasks requiring fusing visual and textual information.However,the current evaluation benchmarks fail to adequately assess the knowledge alignment between images and text,focusing primarily on answer accuracy rather than the reasoning processes behind them.To address this gap and enhance the understanding of LVLMs’capabilities,we introduce KnowBench,a novel benchmark designed to assess the alignment of knowledge between images and text for LVLMs.KnowBench comprises 1081 image-question pairs,each with four options and four pieces of corresponding knowledge across 11 major categories.We evaluate mainstream LVLMs on KnowBench,including proprietary models like Gemini,Claude,and GPT,and open-source models like LLaVA,Qwen-VL,and InternVL.Our experiments reveal a notable discrepancy in the models’abilities to select correct answers and corresponding knowledge whether the models are opensource or proprietary.This indicates that there is still a significant gap in the current LVLMs’knowledge alignment between images and text.Furthermore,our further analysis shows that model performance on KnowBench improves with increased parameters and version iterations.This indicates that scaling laws have a significant impact on multimodal knowledge alignment,and the iteration of the model by researchers also has a positive effect.We anticipate that KnowBench will foster the development of LVLMs and motivate researchers to develop more reliable models.We have made our dataset publicly available at http://gffzzd3cc09b8251d45dfsf0c6fb6xxoxf690u.ffgz.tsg.suse.edu.cn/10.57760/sciencedb.29672. 展开更多
关键词 large visual language model(LVLM) knowledge alignment image and text fusing evaluation benchmark
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面向知识融合的本草典籍知识图谱实体对齐研究 认领 引用 被引量：1
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作者 李贺 邵文诗 +3 位作者 刘嘉宇 张津源 沈旺 王桂敏 《现代情报》 CSSCI 北大核心 2026年第3期30-43,共14页
[目的/意义]针对本草典籍知识图谱实体对齐任务中图谱异构、术语易混淆及高质量标注稀缺等挑战,提出融合生成对抗网络与模糊语义辨识的实体对齐模型GAFL-Align,旨在实现多源知识自动化融合。[方法/过程]该模型通过BERT与图注意力网络融... [目的/意义]针对本草典籍知识图谱实体对齐任务中图谱异构、术语易混淆及高质量标注稀缺等挑战,提出融合生成对抗网络与模糊语义辨识的实体对齐模型GAFL-Align,旨在实现多源知识自动化融合。[方法/过程]该模型通过BERT与图注意力网络融合实体语义与拓扑结构,利用生成对抗网络进行领域自适应以消除异构引发的特征分布差异,采用模糊边界负采样策略强化对易混淆术语的细粒度辨识,并结合迭代自训练机制利用高置信度结果扩充样本,有效降低对人工标注的依赖。[结果/结论]实验表明,该模型在自建数据集上的核心指标均优于基线方法。在此基础上构建的多源融合图谱实现了典籍间知识的互补与增值,为本草典籍知识自动化融合提供了有力的技术支撑。 展开更多
关键词 知识融合 实体对齐 本草典籍 知识图谱 深度学习
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无对齐实体场景的多语言知识图谱补全 认领 引用 被引量：1
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作者 唐榕氚 徐秋程 +2 位作者 汤闻易 翟飞飞 周玉 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2026年第1期252-259,共8页
多语言知识图谱补全(MKGC)旨在利用其他语言知识图谱的信息增强目标语言知识图谱上的链接预测性能。现有方法通常利用不同语言知识图谱之间预先对齐的实体对作为知识迁移的媒介,然而在实际场景中,不同语言知识图谱之间通常没有预先对齐... 多语言知识图谱补全(MKGC)旨在利用其他语言知识图谱的信息增强目标语言知识图谱上的链接预测性能。现有方法通常利用不同语言知识图谱之间预先对齐的实体对作为知识迁移的媒介,然而在实际场景中,不同语言知识图谱之间通常没有预先对齐的实体,导致难以实现知识迁移。针对上述无对齐实体场景,提出一种融合预训练语言模型信息的伪对齐实体生成模块,不断迭代生成新的对齐实体进行知识迁移。为区分不同语言知识图谱中信息对目标语言知识图谱的贡献度,提出一种基于多图注意力的图神经网络(MGA-GNN)用于对三元组进行编码,通过该网络输出的嵌入表征计算得到三元组的合理性得分,完成链接预测任务。为验证所提方法的有效性,在2个公开数据集DBP-5L和E-PKG上进行了实验验证,结果表明:所提方法在多个语言知识图谱上链接预测的性能超过了有对齐实体的MKGC方法,证明了该方法在更加实际场景下的优越性能。 展开更多
关键词 多语言知识图谱补全 实体对齐 多图注意力 图神经网络 链接预测
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技能适配与就业结构升级——基于人工智能的研究视角 认领 引用 被引量：1
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作者 刘贝玙 平萍 《统计学报》 2026年第6期26-37,共12页
基于2016—2024年全国162个地级及以上城市面板数据,运用大语言模型测度高职专业设置与技术变革方向的契合程度,构建城市层面的技能适配指数,并结合企业招聘大数据考察其对就业结构升级的影响。研究发现,技能适配显著促进了城市就业结... 基于2016—2024年全国162个地级及以上城市面板数据,运用大语言模型测度高职专业设置与技术变革方向的契合程度,构建城市层面的技能适配指数,并结合企业招聘大数据考察其对就业结构升级的影响。研究发现,技能适配显著促进了城市就业结构升级。机制检验表明,技能适配通过加快知识更新、促进技术扩散发挥作用。异质性分析显示,该效应在“非双高”城市、高智能化城市以及高厚度劳动力市场中更为显著。 展开更多
关键词 人工智能 技能适配 就业结构 职业教育 专业设置 知识更新 技术扩散 劳动力市场
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基于表示学习的跨学科概念关联研究 认领 引用
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作者 黄京 张光照 王忠义 《现代情报》 CSSCI 北大核心 2026年第2期172-184,共13页
[目的/意义]本研究旨在解决概念在多阶语义关系的深度表示学习和跨学科关联中的问题,以突破传统方法的表层特征匹配局限。[方法/过程]本文基于学科概念知识图谱,提出了跨学科概念关联方法,该方法借助基于表示学习的知识对齐模型,综合语... [目的/意义]本研究旨在解决概念在多阶语义关系的深度表示学习和跨学科关联中的问题,以突破传统方法的表层特征匹配局限。[方法/过程]本文基于学科概念知识图谱,提出了跨学科概念关联方法,该方法借助基于表示学习的知识对齐模型,综合语法、语义和语用上的相关性,捕捉学科知识图谱中隐含的结构关联特征,构建面向跨学科知识服务的概念关联模型。[结果/结论]本文以“隐私保护”领域为实验对象进行测试,验证了基于表示学习的跨学科概念关联方法的有效性。 展开更多
关键词 跨学科 概念知识融合 知识表示学习 实体对齐 概念关联
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国家在线精品课程旅游资源地理的课程建设实践 认领 引用 被引量：1
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作者 曹培培 《武汉工程职业技术学院学报》 2026年第1期88-91,共4页
旅游资源地理课程入选2023年职业教育国家在线精品课程。课程建设遵循“实践驱动知识内化、课材携手赋能教学、社会服务反哺课堂”的逻辑主线,系统推进教学改革,力求破解教学痛点,推动理论知识落地生根,实践教学提质增效,社会服务深化拓... 旅游资源地理课程入选2023年职业教育国家在线精品课程。课程建设遵循“实践驱动知识内化、课材携手赋能教学、社会服务反哺课堂”的逻辑主线,系统推进教学改革,力求破解教学痛点,推动理论知识落地生根,实践教学提质增效,社会服务深化拓展,从而构建课程与教材良性互动的优质教学生态,为同类课程建设提供可资借鉴的实践路径。 展开更多
关键词 旅游资源地理 知行合一 社会服务 精品课程 旅游专业 课程建设
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基于多源知识跨模态对齐与融合的医学报告生成方法 认领 引用
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作者 张晓丹 王博岳 +3 位作者 王锡文 孙善斌 李晓理 刘兆会 《北京工业大学学报》 CAS CSCD 北大核心 2026年第8期858-869,共12页
针对医学影像和报告在数据内容和数据分布2个层面存在的数据偏差问题,提出一种基于多源知识跨模态对齐与融合的医学报告生成方法(medical report generation method based on multi-source knowledge cross-modal alignment and fusion,... 针对医学影像和报告在数据内容和数据分布2个层面存在的数据偏差问题,提出一种基于多源知识跨模态对齐与融合的医学报告生成方法(medical report generation method based on multi-source knowledge cross-modal alignment and fusion,MRGM-MKCAF)。首先,将放射学相关的医学概念作为先验知识,并将案例影像和报告作为经验知识,构建多模态知识库;然后,提出一种基于多头注意力机制的跨模态对齐方法,利用多个注意力头分别关注知识库中多种模态特征的子空间,并进行跨模态细粒度特征对齐,获得更加全面准确的编码特征;最后,提出一种基于门控机制的解码模块,对多源编码特征进行动态的特征选取,去除冗余和无关信息,进而用于报告生成。在2组数据集上的实验结果表明,提出的方法显著提高了生成报告的准确性、流畅性和完整性。 展开更多
关键词 医学报告生成 多源知识 跨模态对齐 跨模态融合 注意力机制 医学影像
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