Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete. We explored iterative methods, using the results as new seeds. In this paper we address the following research questions:
How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds?
How does the reconstructed domain knowledge spread geographically?
Can the method be used to inspect the past, present, and future of knowledge?
Can the method be used to find emerging knowledge?
I collected here the list of my write-ups of the first three keynote speeches of the conference: Human in the Loop Machine Learning (Carla E. Brodley, Northeastern Univ.) Enhancing Human Perception via Text Mining and IR (Cheng Zhai, Univ. Illinois) Graph Representation Learning (Jure Leskovec, Stanford and Pinterest)
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance … Continue reading Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information through Unsupervised Learning
FaST – Fashion Sensing Technology – is a project meant to design, experiment with, and implement an ICT tool that could monitor and analyze the activity of Italian emerging Fashion brands on social media.