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Wang-Chiew Tan: Subjective Databases

Speaker Name: 
Wang-Chiew Tan
Speaker Organization: 
Megagon Labs
Start Time: 
Thursday, October 8, 2020 - 1:50pm
End Time: 
Thursday, October 8, 2020 - 2:50pm
Zoom (registration required at
Center for Research in Open Source Software

This talk is presented as part of the 2020 CROSS Symposium Workshop on Aspects of Data Management. To join this talk, please register here.



Abstract: Subjective data refers to data that contains opinions and experiences. Such data is ubiquitous in product reviews, tweets, and discussion forums in social media. Consumers today spend considerable time sifting through subjective data to make informed decisions about purchases. At Megagon Labs, we are building technologies to synthesize knowledge from subjective data and to facilitate searching over them. In this talk, I will describe how consumers can enjoy an enhanced search experience through a system that directly supports both subjective and objective search; by harnessing information "outside the box", in the text of online reviews or social media, managing the extractions as first-class citizens in a data management system, and answering subjective queries.



Bio: Wang-Chiew Tan leads the research efforts at Megagon Labs with the goal of building advanced technologies to enhance search by experience. Her team conducts research on data integration, information extraction, text mining and summarization, knowledge base construction and commonsense reasoning, and data visualization. Prior to that, she was a Professor of Computer Science at University of California, Santa Cruz. She also spent two years at IBM Research - Almaden. She received her B.Sc. (First Class) in Computer Science from the National University of Singapore and her Ph.D. in Computer Science from the University of Pennsylvania. Her research interests include data integration and exchange, data provenance, and natural language processing.  Wang-Chiew Tan is an ACM Fellow, Winner of the 2019 VLDB Women in Database Research Award, and co-winner of the 2014 PODS Test-of-Time Award and the 2018 ICDT Test-of-Time Award.



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