Entity-based Data Science

There is a growing interest in integrating, analyzing, visualizing and making sense of large collections structured, semi-structured and unstructured data. In the world of big data, data science provides tools to help with this process – tools for cleaning the data, tools for integrating and aligning the data, tools for finding patterns in the data and making predictions, and tools for visualizing and interacting with the data. In this talk, I will focus on entity-based data science, data science techniques which support the tasks of entity resolution (determining when two references refer to the same entity), collective classification (predicting missing entity labels), and link prediction (predicting relationships) in a holistic manner which takes into account both entity attributes and relationships among the entities. I will overview of our recent work on probabilistic soft logic (PSL), a framework for collective, probabilistic reasoning in relational domains. Our recent results show that by using state-of-the-art optimization methods in a distributed implementation, we can solve large-scale problems with millions of random variables orders of magnitude more quickly than existing approaches.

Speaker Bio: Lise Getoor a professor in the Computer Science Department at the University of Maryland, College Park. Her primary research interests are in machine learning and reasoning with uncertainty, applied to graphs and structured data. She also works in data integration, social network analysis and visual analytics. She has six best paper awards, an NSF Career Award, and is an Association for the Advancement of Artificial Intelligence (AAAI) Fellow. She has served as action editor for the Machine Learning Journal, JAIR associate editor, and TKDD associate editor. She is a board member of the International Machine Learning Society, has been a member of AAAI Executive council, was PC co-chair of ICML 2011, and has served as senior PC member for conferences including AAAI, ICML, IJCAI, ISWC, KDD, SIGMOD, UAI, VLDB, WSDM and WWW. She received her Ph.D. from Stanford University, her M.S. from UC Berkeley, and her B.S. from UC Santa Barbara. For more information, see http://www.cs.umd.edu/~getoor.

Presentation Slides: PDF

Note: Visitors from outside NIST must contact Cathy Graham; (301) 975-3800; at least 24 hours in advance.