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/StephanieWyt/RDGCN
공식 코드Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
https://github.com/THU-KEG/Entity_Alignment_Papers
공식 코드https://github.com/THUDM/SelfKG
공식 코드https://github.com/ThomasBrouwer/HMF
공식 코드https://github.com/UCI-Networking-Group/AutoFR
공식 코드https://github.com/VDIGPKU/QGCN
공식 코드Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
https://github.com/Valentyn1997/kg-alignment-lessons-learned
공식 코드Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
https://github.com/WanzhengZhu/GPS
공식 코드Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech
https://github.com/Wyb0627/KBLink
공식 코드KGLink: A column type annotation method that combines knowledge graph and pre-trained language model
https://github.com/ZhonghaoZ/AMP-Net_TIP
공식 코드AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
https://github.com/ZihengZZH/OntoEA
공식 코드OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
https://github.com/ZihengZZH/industry-eval-EA
공식 코드https://github.com/aamirshafi/distributed
공식 코드Efficient MPI-based Communication for GPU-Accelerated Dask Applications
https://github.com/acabassi/combine-psms
공식 코드Kernel learning approaches for summarising and combining posterior similarity matrices
https://github.com/acharkq/training-free-graph-matching
공식 코드https://github.com/agostini335/mmvmvae-mimic
공식 코드https://github.com/ahmed-sharshar/respirodynamics
공식 코드PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion
https://github.com/ai4society/infospread-neurips-24
공식 코드Towards Effective Planning Strategies for Dynamic Opinion Networks
https://github.com/ailab-cefet-rj/fusion2025
공식 코드Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting
https://github.com/aim-skku/box-is
공식 코드IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks
https://github.com/aim-skku/nyudv2-is
공식 코드IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks
https://github.com/aim-skku/sun-rgbd-is
공식 코드IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks
https://github.com/alan-turing-institute/SemAIDA
공식 코드ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
https://github.com/alexZeakis/Embeddings4ER
공식 코드https://github.com/amazon-research/pizza-semantic-parsing-dataset
공식 코드PIZZA: A new benchmark for complex end-to-end task-oriented parsing
https://github.com/amazon-science/pizza-semantic-parsing-dataset
공식 코드PIZZA: A new benchmark for complex end-to-end task-oriented parsing
https://github.com/arunreddymandle9/Toxic-comments-on-social-media
공식 코드Classification of social media Toxic comments using Machine learning models
https://github.com/ascherp/semantic-web-primer
공식 코드https://github.com/bald6354/tore_volumes
공식 코드https://github.com/bapi/lpp-sgd
공식 코드Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD
https://github.com/bargenossar/flexer
공식 코드https://github.com/bargenossar/the-battleship-approach-to-al-of-em-problem
공식 코드The Battleship Approach to the Low Resource Entity Matching Problem
https://github.com/benevolentai/comp
공식 코드Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
https://github.com/bio-ontology-research-group/walking-rdf-and-owl
공식 코드Neuro-symbolic representation learning on biological knowledge graphs
https://github.com/blzhu0823/pathfusion
공식 코드Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.