donatellafirmani - PhD Courses

General

Title: Semantic approaches for Entity Resolution
Instructor: Donatella Firmani
When: March-April 2024
Enroll: Please fill this form https://forms.gle/Njw79WL2pYxZVsgc7
Duration: 15 hours
In-person participation: Room location will be sent by email to participants.
Remote participation: https://uniroma1.zoom.us/j/87347701551?pwd=dmt1UC85WUxON0RxNFREeUlDNHZYUT09
Audience: The course is included in the National PhD program in Artificial Intelligence and it is open to PhD students and young researchers from any degree-granting institution.
Evaluation: Individual project or written test on the topics covered.
Primary material: Readings in the form of research papers and on-line resources referenced by the instructor.
[Materiale esercitazione Google sites] https://sites.google.com/view/centroestivo/home-page

Abstract

Entity Resolution (ER) is a fundamental task in data management and has long been of interest to many researchers and practitioners. ER aims to identify and match different representations of the same real-world entities across various data sources. Domains such as the World Wide Web can provide thousands of data sources about real-world entities, including profiles of people and institutions, or specifications of products and services. Matching entities in these scenarios can be challenging for automated strategies but is relatively easier for expert humans.

This course covers recent advancements in leveraging human knowledge for ER, including oracle-based approaches, statistical methods, and machine learning techniques like Large Language Models (LLMs). Attendees will also learn methods for producing explanations of ER outcomes, correcting biases, and practical applications such as building taxonomies. All topics addressed during the course are explored from both practical and theoretical perspectives.

Program

[18 Mar 2024] Introduction and Similarity Functions Slides
[18 Mar 2024] Blocking and Clustering Slides
[20 Mar 2024] Theory of Record Linkage 1 Slides
[20 Mar 2024] Theory of Record Linkage 2 Slides
[28 Mar 2024] Machine Learning Approaches Slides
[28 Mar 2024] Explanation and Fairness Slides
[11 Apr 2024] Taxonomies and Knowledge Graphs Slides

References

Fellegi, Ivan P., and Alan B. Sunter. “A Theory for Record Linkage.” Journal of the American Statistical Association, vol. 64, no. 328, 1969, pp. 1183–210. JSTOR, https://doi.org/10.2307/2286061. Accessed 24 Dec. 2023.
Steorts, Rebecca C. "Entity resolution with empirically motivated priors." (2015): 849-875.
Donatella Firmani, Barna Saha, Divesh Srivastava: Online Entity Resolution Using an Oracle. Proc. VLDB Endow. 9(5): 384-395 (2016)
Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava: Robust Entity Resolution using Random Graphs. SIGMOD Conference 2018: 3-18
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, Wang-Chiew Tan: Deep Entity Matching with Pre-Trained Language Models. CoRR abs/2004.00584 (2020)
Dezhong Yao, Yuhong Gu, Gao Cong, Hai Jin, and Xinqiao Lv. 2022. Entity Resolution with Hierarchical Graph Attention Networks. In Proceedings of the 2022 International Conference on Management of Data (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 429–442. https://doi.org/10.1145/3514221.3517872
Raul Castro Fernandez, Aaron J. Elmore, Michael J. Franklin, Sanjay Krishnan, and Chenhao Tan. 2023. How Large Language Models Will Disrupt Data Management. Proc. VLDB Endow. 16, 11 (July 2023), 3302–3309. https://doi.org/10.14778/3611479.3611527
Papadakis, George et al. “Meta-Blocking: Taking Entity Resolutionto the Next Level.” IEEE Transactions on Knowledge and Data Engineering 26 (2014): 1946-1960.
Guilherme Dal Bianco, Marcos André Gonçalves, Denio Duarte: BLOSS: Effective meta-blocking with almost no effort. Inf. Syst. 75: 75-89 (2018)
Amr Ebaid, Saravanan Thirumuruganathan, Walid G. Aref, Ahmed K. Elmagarmid, Mourad Ouzzani: EXPLAINER: Entity Resolution Explanations. ICDE 2019: 2000-2003
Teofili, T., Firmani, D., Koudas, N., Martello, V., Merialdo, P., & Srivastava, D. (2022, May). Effective explanations for entity resolution models. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) (pp. 2709-2721). IEEE.
Efthymiou, V., Stefanidis, K., Pitoura, E., & Christophides, V. (2021, October). FairER: entity resolution with fairness constraints. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3004-3008).
Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava: Hierarchical Entity Resolution using an Oracle. SIGMOD Conference 2022: 414-428
Bar Genossar, Roee Shraga, and Avigdor Gal. 2023. FlexER: Flexible Entity Resolution for Multiple Intents. Proc. ACM Manag. Data 1, 1, Article 42 (May 2023), 27 pages. https://doi.org/10.1145/3588722