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- research-articleFebruary 2024
Adaptive deep learning for entity disambiguation via knowledge-based risk analysis
Expert Systems with Applications: An International Journal (EXWA), Volume 238, Issue PEMar 2024https://doi.org/10.1016/j.eswa.2023.122342AbstractThe state-of-the-art performance on entity disambiguation has been reached by deep neural networks. However, the task remains very challenging due to the complexity of natural language. Moreover, the target data distribution is often different ...
- research-articleNovember 2021
Attention-Enhanced Gradual Machine Learning for Entity Resolution
IEEE Intelligent Systems (IEEECS-INTELLI-NEW), Volume 36, Issue 6Nov.-Dec. 2021, Pages 71–79https://doi.org/10.1109/MIS.2021.3077265Recent work has shown that entity resolution (ER) can be effectively performed by gradual machine learning (GML). GML begins with some automatically labeled easy instances and, then, gradually labels more challenging instances by iterative factor graph ...
Towards Interpretable and Learnable Risk Analysis for Entity Resolution
SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of DataJune 2020, Pages 1165–1180https://doi.org/10.1145/3318464.3380572Machine-learning-based entity resolution has been widely studied. However, some entity pairs may be mislabeled by machine learning models and existing studies do not study the risk analysis problem -- predicting and interpreting which entity pairs are ...
- research-articleFebruary 2020
<italic>r</italic>-HUMO: A Risk-Aware Human-Machine Cooperation Framework for Entity Resolution with Quality Guarantees
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 32, Issue 2Feb. 2020, Pages 347–359https://doi.org/10.1109/TKDE.2018.2883532Even though many approaches have been proposed for entity resolution (ER), it remains very challenging to enforce quality guarantees. To this end, we propose a <italic>r</italic>isk-aware HUman-Machine cOoperation framework for ER, denoted by <italic>r</...
- research-articleMay 2019
Gradual Machine Learning for Entity Resolution
WWW '19: The World Wide Web ConferenceMay 2019, Pages 3526–3530https://doi.org/10.1145/3308558.3314121Usually considered as a classification problem, entity resolution can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most notably deep neural ...
- research-articleAugust 2018
Improving Machine-based Entity Resolution with Limited Human Effort: A Risk Perspective
BIRTE '18: Proceedings of the International Workshop on Real-Time Business Intelligence and AnalyticsAugust 2018, Article No.: 4, Pages 1–5https://doi.org/10.1145/3242153.3242156Pure machine-based solutions usually struggle in the challenging classification tasks such as entity resolution (ER). To alleviate this problem, a recent trend is to involve the human in the resolution process, most notably the crowdsourcing approach. ...