Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment
Entity alignment (EA) identifies entities across knowledge graphs (KGs) that refer to the same real-world object. Conventional EA methods mainly exploit explicit graph structures and textual fields, which often provide i…
Entity AlignmentKnowledge GraphsRepresenting Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority
AI knowledge systems require representations of entity importance for retrieval, recommendation, evidence selection, and knowledge-intensive reasoning. Yet importance is often reduced to a single score derived from eithe…
Entity AlignmentFrom Region Arrival to Instance-Level Grounding in Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) agents may satisfy conventional success criteria while still failing to establish reliable object-level grounding, because current evaluation protocols mainly reward stopping within a…
Visual GroundingEntity AlignmentPredicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment
Knowledge graphs (KGs) are increasingly used as structured context for Large Language Models (LLMs), but industrial KG-RAG systems often need to integrate public and domain-specific KGs constructed from heterogeneous dat…
Knowledge GraphsEntity AlignmentHarnessing Structural Context for Entity Alignment Foundation Models
Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cross-KG reasoning. The recent EA foundation model demonstrates that a…
Knowledge GraphsEntity AlignmentHELEA: Hard-Negative Benchmark and LLM-based Reranking for Robust Entity Alignment
Entity Alignment (EA) is essential for knowledge graph (KG) fusion, but existing benchmarks often allow models to exploit name overlap rather than relational structure. This makes it difficult to evaluate whether models …
Entity AlignmentQuery-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning
Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as a static matching problem, ignoring query context and cross-system …
Representation LearningKnowledge GraphsEntity AlignmentSherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where all participants share the same feature sp…
Federated LearningEntity AlignmentFraud DetectionDebate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate
Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based on large language models (LLMs) typically obtain entity embeddings …
Representation LearningKnowledge GraphsEntity AlignmentDecision MakingOpen-Source Reproduction and Explainability Analysis of Corrective Retrieval Augmented Generation
Corrective Retrieval Augmented Generation (CRAG) improves the robustness of RAG systems by evaluating retrieved document quality and triggering corrective actions. However, the original implementation relies on proprieta…
Semantic SimilarityEntity AlignmentLooking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation
Although large vision-language models (LVLMs) have demonstrated remarkable capabilities, they are prone to hallucinations in multi-image tasks. We attribute this issue to limitations in existing attention mechanisms and …
Entity AlignmentPSQE: A Theoretical-Practical Approach to Pseudo Seed Quality Enhancement for Unsupervised Multimodal Entity Alignment
Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different data modalities, enabling structural data integration that in turn improves the performance of various large language model applica…
Contrastive LearningEntity AlignmentBreaking the Reasoning Horizon in Entity Alignment Foundation Models
Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using graph foundation models (GFMs) offer a s…
Entity AlignmentLink PredictionFastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation
Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but current methods remain inefficient and oft…
Visual ReasoningEntity AlignmentCross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
Product matching aims to identify identical or similar products sold on different platforms. By building knowledge graphs (KGs), the product matching problem can be converted to the Entity Alignment (EA) task, which aims…
Knowledge GraphsEntity AlignmentOn the Entity-Level Alignment in Crosslingual Consistency
Multilingual large language models (LLMs) are expected to recall factual knowledge consistently across languages. However, the factors that give rise to such crosslingual consistency -- and its frequent failure -- remain…
Entity AlignmentMultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional machine learning methods for capturing i…
Semantic SimilarityKnowledge GraphsEntity AlignmentLink PredictionZero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective
Cross-lingual Named Entity Recognition (CL-NER) aims to transfer knowledge from high-resource languages to low-resource languages. However, existing zero-shot CL-NER (ZCL-NER) approaches primarily focus on Latin script l…
Cross-Lingual NEREntity AlignmentESNERA: Empirical and semantic named entity alignment for named entity dataset merging
Named Entity Recognition (NER) is a fundamental task in natural language processing. It remains a research hotspot due to its wide applicability across domains. Although recent advances in deep learning have significantl…
Entity AlignmentInterpretable Modeling of Driver Attention Shifts with a Vision-Language Model
Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being monitored or why an attention shift may matt…
Entity Alignment