Entity Alignment
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Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sutaoyu/CDMEA
공식 코드Mitigating Modality Bias in Multi-modal Entity Alignment from a Causal Perspective
https://github.com/sutaoyu/IBMEA
공식 코드IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
https://github.com/sutaoyu/LoginMEA
공식 코드LoginMEA: Local-to-Global Interaction Network for Multi-modal Entity Alignment
https://github.com/thunlp/MuGNN
공식 코드https://github.com/thunlp/explore-and-evaluate
공식 코드Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
https://github.com/tigerchen52/pearl
공식 코드Learning High-Quality and General-Purpose Phrase Representations
https://github.com/txb-nju/exea
공식 코드Generating Explanations to Understand and Repair Embedding-based Entity Alignment
https://github.com/uq-neusoft-health-data-science/activeea
공식 코드https://github.com/uqbingliu/divea
공식 코드https://github.com/uqbingliu/emea
공식 코드https://github.com/uqbingliu/stea
공식 코드https://github.com/visual-text-QA/VTQA-Demo
공식 코드VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
https://github.com/wondercs1213/uniea
공식 코드Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
https://github.com/wyy-code/TFP
공식 코드Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
https://github.com/wyy-code/desalign
공식 코드Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
https://github.com/wyy-code/pipea
공식 코드Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
https://github.com/xiaoqian19940510/moalign
공식 코드Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
https://github.com/xiefeng69/gaea
공식 코드Improving Knowledge Graph Entity Alignment with Graph Augmentation
https://github.com/xiefeng69/mixtea
공식 코드MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
https://github.com/xtra-computing/fet
공식 코드Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
https://github.com/yifan-h/multilingual_space
공식 코드Adapters for Enhanced Modeling of Multilingual Knowledge and Text
https://github.com/yijia-xiao/know2bio
공식 코드Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs
https://github.com/zhurboo/RAGA
공식 코드RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
https://github.com/zju-daily/dualmatch
공식 코드https://github.com/zjukg/MEAformer
공식 코드MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
https://github.com/zjukg/kg-mm-survey
공식 코드Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/snag
공식 코드Noise-powered Multi-modal Knowledge Graph Representation Framework
https://github.com/1049451037/GCN-Align
https://github.com/China-UK-ZSL/ZS-F-VQA
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/CogComp/JEANS
https://github.com/Koukyosyumei/AIJack
https://github.com/MaoXinn/MRAEA
https://github.com/Peter7Yao/RNM
Relation-Aware Neighborhood Matching Model for Entity Alignment
https://github.com/hackerchenzhuo/LaKo
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/liyichen-cly/MSNEA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.