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Data Integration
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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/maoxinn/seu
공식 코드From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
https://github.com/martanit/LEAD
공식 코드Effective Model of Loop Extrusion Predicts Chromosomal Domains
https://github.com/maulikkamdar/LSLODQuery
공식 코드An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on the Web
https://github.com/maxkinny/tabrecset
공식 코드A large-scale dataset for end-to-end table recognition in the wild
https://github.com/mberr/ea-active-learning
공식 코드https://github.com/mberr/ea-sota-comparison
공식 코드A Critical Assessment of State-of-the-Art in Entity Alignment
https://github.com/mberr/rank-based-evaluation
공식 코드On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
https://github.com/megagonlabs/ditto
공식 코드https://github.com/megagonlabs/doduo
공식 코드https://github.com/megagonlabs/sato
공식 코드https://github.com/mehrzadshm/built-bench-paper
공식 코드Benchmarking pre-trained text embedding models in aligning built asset information
https://github.com/menidata1/mimic_2_omop
공식 코드https://github.com/metaagentx/opencaptchaworld
공식 코드https://github.com/mhmoslemi2338/pre-EM-bias
공식 코드https://github.com/mhmoslemi2338/sigmod-FAIR-EM-post-process
공식 코드Reducing Biases in Record Matching Through Scores Calibration
https://github.com/micdoh/XLRON
공식 코드Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?
https://github.com/microsoft/StyleSwin
공식 코드StyleSwin: Transformer-based GAN for High-resolution Image Generation
https://github.com/microsoft/dcvc
공식 코드https://github.com/microsoft/hitab
공식 코드HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
https://github.com/mitao-cat/feature-level_bias
공식 코드Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
https://github.com/mitdbg/kramabench
공식 코드KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
https://github.com/mitmedialab/sherlock-project
공식 코드Sherlock: A Deep Learning Approach to Semantic Data Type Detection
https://github.com/mkyl/chorus
공식 코드CHORUS: Foundation Models for Unified Data Discovery and Exploration
https://github.com/mullervasconcelos/ieee-tac2023
공식 코드Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
https://github.com/naserahmadi/tdmatch
공식 코드https://github.com/newiz430/bandana
공식 코드https://github.com/ngmarchant/dblink
공식 코드https://github.com/ninibymilk/pmf-mmea
공식 코드Progressively Modality Freezing for Multi-Modal Entity Alignment
https://github.com/nju-websoft/AliNet
공식 코드Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
https://github.com/nju-websoft/HyperKA
공식 코드Knowledge Association with Hyperbolic Knowledge Graph Embeddings
https://github.com/nju-websoft/JAPE
공식 코드Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
https://github.com/nju-websoft/MultiKE
공식 코드https://github.com/nju-websoft/OpenEA
공식 코드A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
https://github.com/nju-websoft/RSN
공식 코드Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
https://github.com/nju-websoft/Remp
공식 코드Crowdsourced Collective Entity Resolution with Relational Match Propagation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.