https://github.com/ruizhang-ai/EA_for_KG
공식 코드A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning
Papers with Code 아카이브
Data Integration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 0
- 보존된 평가 기록
- 0
Data integration (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation. Surveys on Data integration:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ruizhang-ai/autoalign
공식 코드AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
https://github.com/rustadadam/mashspud
공식 코드https://github.com/rusty1s/deep-graph-matching-consensus
공식 코드https://github.com/s-mohammad-hashemi/sst
공식 코드https://github.com/sajqavril/Code-Repo-for-R-GCN-Auto-Encoder
공식 코드Type Information-Assisted Self-Supervised Knowledge Graph Denoising
https://github.com/salesforce/alpro
공식 코드Align and Prompt: Video-and-Language Pre-training with Entity Prompts
https://github.com/sbhattacharyay/dynamic_gose_model
공식 코드Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
https://github.com/sdm-tib/eablock
공식 코드EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines
https://github.com/shawntanzk/cl-manuscript
공식 코드https://github.com/shraga89/MED
공식 코드https://github.com/shraga89/powarematch
공식 코드PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching
https://github.com/shyamven/learn2mix
공식 코드Learn2Mix: Training Neural Networks Using Adaptive Data Integration
https://github.com/simonvino/fusl
공식 코드Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI
https://github.com/siwangzhou/deepbcs
공식 코드Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
https://github.com/skyhehe123/scatterformer
공식 코드ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention
https://github.com/sntcristian/and-kge
공식 코드A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
https://github.com/sociovestix/lenu
공식 코드https://github.com/soda-inria/carte
공식 코드https://github.com/soledad921/tea-gnn
공식 코드Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
https://github.com/soojiny/eralign
공식 코드https://github.com/spring-epfl/sinbad
공식 코드SINBAD: Saliency-informed detection of breakage caused by ad blocking
https://github.com/squareroot3/fusedgw-entity-alignment
공식 코드A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
https://github.com/stanford-oval/dialogues
공식 코드X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
https://github.com/sunblaze-ucb/progent
공식 코드https://github.com/sunflower-the-cat/DAEA
공식 코드https://github.com/sunlab-osu/TURL
공식 코드https://github.com/superhero-7/altdiffuson
공식 코드https://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/tablegpt/tablegpt-agent
공식 코드TableGPT2: A Large Multimodal Model with Tabular Data Integration
https://github.com/tdelgado00/learning-synthesis
공식 코드Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach
https://github.com/thu-lyj-lab/o2-recon
공식 코드https://github.com/thudm/whoiswho
공식 코드Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit
https://github.com/thunlp/MuGNN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.