https://bitbucket.org/params/dsmlr
공식 코드DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Papers with Code 아카이브
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/13998151318/nala
공식 코드NALA: an Effective and Interpretable Entity Alignment Method
https://github.com/AI4WA/Docs2KG
공식 코드https://github.com/ArjitJ/DIAL
공식 코드Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
https://github.com/BharathMuppasani/NeurIPS-Submission-2024
공식 코드Towards Effective Planning Strategies for Dynamic Opinion Networks
https://github.com/BordiaS/LegalLens_inference
공식 코드https://github.com/D-ST-Sword/ATD
공식 코드Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
https://github.com/DOE-NCI-Pilot1/CCLFeatureComparison
공식 코드A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
https://github.com/DexterZeng/CEA
공식 코드https://github.com/DexterZeng/CEAFF
공식 코드Reinforcement Learning based Collective Entity Alignment with Adaptive Features
https://github.com/DexterZeng/DAT
공식 코드https://github.com/DexterZeng/UEA
공식 코드Towards Entity Alignment in the Open World: An Unsupervised Approach
https://github.com/ECNU-Text-Computing/LDRI
공식 코드https://github.com/GemsLab/node2bits
공식 코드node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
https://github.com/GeorgeMichailidis/JMMLE_code
공식 코드https://github.com/HadiJahanshahi/DABT
공식 코드https://github.com/Handon112358/NeurIPS_2024_Learning-Matchable-Prior-For-Entity-Alignment-with-Unlabeled-Dangling-Cases
공식 코드Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
https://github.com/HelikarLab/MADRID
공식 코드COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery
https://github.com/IDEA-FinAI/Simple-HHEA
공식 코드https://github.com/Jantory/anymatch
공식 코드AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model
https://github.com/JieJieNiu/SmaAt-fUsion-and-SmaAt-Krige-GNet
공식 코드https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb
공식 코드https://github.com/LinXueyuanStdio/EchoEA
공식 코드EchoEA: Echo Information between Entities and Relations for Entity Alignment
https://github.com/LiriFang/knowledge-augmented-entity-resolution
공식 코드T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework
https://github.com/Living-with-machines/DeezyMatch
공식 코드A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
https://github.com/Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching
공식 코드A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
https://github.com/MaoXinn/Dual-AMN
공식 코드https://github.com/MaoXinn/RREA
공식 코드https://github.com/NICTA/serene-benchmark
공식 코드https://github.com/NYXFLOWER/GripNet
공식 코드GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
https://github.com/OscarYau525/FSPDA
공식 코드A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
https://github.com/RoonakR/rational_model
공식 코드https://github.com/SDM-TIB/SDM-RDFizer
공식 코드EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines
https://github.com/SFIG611/tabbie
공식 코드https://github.com/SeerLabs/entity-matching
공식 코드Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
https://github.com/StephanieWyt/HGCN-JE-JR
공식 코드Jointly Learning Entity and Relation Representations for Entity Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.