Papers Entity Embeddings
“Entity Embeddings” 태그가 달린 논문 151편 · 필터 해제
Universal Embeddings of Tabular Data
Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are oft…
Entity EmbeddingsOutlier DetectionUnderstanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237
FB15k-237 mitigates the data leakage issue by excluding inverse and symmetric relationship triples, however, this has led to substantial performance degradation and slow improvement progress. Traditional approaches demon…
Entity EmbeddingsLink PredictionSE-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 GraphsPathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications. KG embeddings are an effective approach to addressing tasks like knowledge dis…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction+1LLM-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+2JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase
Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading…
Entity EmbeddingsEntity LinkingFraud DetectionKnowledge GraphsDyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval accuracy and limits the model's ability…
Document RankingEntity EmbeddingsEntity RetrievalRetrieval+1DERA: 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+2RaTEScore: A Metric for Radiology Report Generation
This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text) Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models. RaTEScore emphasizes crucial medical entit…
DiagnosticEntity EmbeddingsLanguage ModelingLanguage Modelling+2Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification
Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. However, existing FKGE methods …
Entity EmbeddingsGraph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsPersonalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs, while concurrently safeguarding the priv…
Entity Embeddingsglobal-optimizationGraph EmbeddingKnowledge Graph Embedding+2Graph Reasoning for Explainable Cold Start Recommendation
The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS). A common solution involves using Knowledge Graphs (KG) to train entity embeddings or…
Entity EmbeddingsKnowledge GraphsRecommendation SystemsOmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product…
Entity EmbeddingsImage CaptioningMulti-Task LearningImproving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by c…
Contrastive LearningDescriptiveDiversityEntity Embeddings+3Unlocking the Power of Large Language Models for Entity Alignment
Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their eff…
Code TranslationEntity AlignmentEntity EmbeddingsRepresentation LearningEmbedding Knowledge Graphs in Degenerate Clifford Algebras
Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have b…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsTwo Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantl…
Entity AlignmentEntity EmbeddingsKnowledge GraphsLanguage Modelling+1Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation lea…
Entity AlignmentEntity EmbeddingsGraph Representation LearningKnowledge Graphs+3Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM). However, a key challenge arises from the fact that relation extraction cannot strai…
Entity EmbeddingsLanguage ModelingLanguage ModellingRelation+5Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predictions have to be made among unseen entit…
Entity EmbeddingsInductive Link PredictionKnowledge GraphsLink Prediction+1