אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

The matching task is at the heart of data integration, in charge of aligning elements of data sources. Matching is a handy tool in multiple contemporary business and commerce applications and has been investigated in the fields of databases, AI, Semantic Web, and data mining for many years. The core challenge still remains the ability to create quality algorithmic matchers, automatic tools for identifying correspondences among data concepts (e.g., database attributes). Matching problems were traditionally performed in a semi-automatic manner, with correspondences being generated by matching algorithms and outcomes subsequently validated by human experts. In this talk, I will discuss the merits of human-in-the-loop data integration with an emphasis on the obstacles of achieving effective human matching and validation. To illustrate the ability of machine learning to support human-in-the-loop, I will present a novel characterization of "human matching experts" and provide a novel framework to identify reliable and valuable human experts. The framework is accompanied by a novel set of features and was shown to be useful using an extensive empirical evaluation. In particular, we show that our approach can improve matching results by electing expert matchers. To conclude the talk, I will elaborate on our recent research including a deep learning mechanism to calibrate and filter human matching decisions to improve the quality of a match. BIO: Roee Shraga is a Postdoctoral fellow at the Technion - Israel Institute of Technology, from which he received a PhD degree in 2020 in the area of Data Science. Roee has published more than a dozen papers in leading journals and conferences on the topics of data integration, human-in-the-loop, machine learning, process mining, and information retrieval. He is also a recipient of several PhD fellowships including the Leonard and Diane Sherman Interdisciplinary Fellowship (2017), the Daniel Excellence Scholarship (2019), and the Miriam and Aaron Gutwirth Memorial Fellowship (2020).