Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs
Large Language Models (LLMs) often suffer from hallucinations, which Retrieval-Augmented Generation (RAG) and GraphRAG mitigate by incorporating external knowledge and knowledge graphs (KGs). However, GraphRAG remains te…
Knowledge GraphsEntity AlignmentEntity LinkingMitigating Modality Bias in Multi-modal Entity Alignment from a Causal Perspective
Multi-Modal Entity Alignment (MMEA) aims to retrieve equivalent entities from different Multi-Modal Knowledge Graphs (MMKGs), a critical information retrieval task. Existing studies have explored various fusion paradigms…
counterfactualEntity AlignmentInformation RetrievalKnowledge Graphs+1SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment
Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs (KGs) and is widely used in graph fusion-related fields. However, as the scale of KGs increases, manua…
Entity AlignmentEntity EmbeddingsGraph Neural NetworkKnowledge GraphsType Information-Assisted Self-Supervised Knowledge Graph Denoising
Knowledge graphs serve as critical resources supporting intelligent systems, but they can be noisy due to imperfect automatic generation processes. Existing approaches to noise detection often rely on external facts, log…
DenoisingEntity Alignmentgraph constructionKnowledge GraphsLLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs
Entity Alignment (EA) seeks to identify and match corresponding entities across different Knowledge Graphs (KGs), playing a crucial role in knowledge fusion and integration. Embedding-based entity alignment (EA) has rece…
Entity AlignmentEntity EmbeddingsHallucinationInstruction Following+2Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
Entity alignment aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contai…
Contrastive LearningEntity AlignmentGraph Neural NetworkKnowledge GraphsSelf-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps
Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various historical research endeavors. However, lin…
Entity AlignmentInstance SegmentationSelf-Supervised LearningSemantic Segmentation+1CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models
Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed about the rapidly evolvin…
ArticlesEntity Alignmentgraph constructionIn-Context Learning+2SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on their embedding similarities using semi-supe…
Entity AlignmentGraph Neural NetworkKnowledge GraphsEffective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies i…
Entity AlignmentKnowledge GraphsFederated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learning (VFL) is particularly relevant in re…
Entity AlignmentFederated LearningVertical Federated LearningMCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment
Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their com…
Entity AlignmentInformation RetrievalKnowledge GraphsManagement+3Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs
Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world KGs, aligned entities usually have non-…
AttributeEntity AlignmentKnowledge GraphsA Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment
The success of current Entity Alignment (EA) task depends largely on the supervision information provided by labeled data. Considering the cost of labeled data, most supervised methods are difficult to apply in practical…
Active LearningContrastive LearningEntity AlignmentKnowledge GraphsNeuro-Symbolic Entity Alignment via Variational Inference
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic models, while precise, struggle with su…
Entity AlignmentKnowledge GraphsVariational InferenceNeurosymbolic Methods for Dynamic Knowledge Graphs
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of e…
Entity AlignmentKnowledge GraphsEnhancing Visual Dialog State Tracking through Iterative Object-Entity Alignment in Multi-Round Conversations
Visual Dialog (VD) is a task where an agent answers a series of image-related questions based on a multi-round dialog history. However, previous VD methods often treat the entire dialog history as a simple text input, di…
dialog state trackingDialogue State TrackingEntity AlignmentVisual DialogDERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs
Entity Alignment (EA) aims to match equivalent entities in different Knowledge Graphs (KGs), which is essential for knowledge fusion and integration. Recently, embedding-based EA has attracted significant attention and m…
AttributeEntity AlignmentEntity EmbeddingsEntity Retrieval+2Aligning Multiple Knowledge Graphs in a Single Pass
Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, a…
Entity AlignmentKnowledge GraphsLoginMEA: Local-to-Global Interaction Network for Multi-modal Entity Alignment
Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs (MMKGs), whose entities can be associated with relational triples and related images. Most previous studie…
Entity AlignmentGraph AttentionKnowledge GraphsMulti-modal Entity Alignment