Scalable Construction and Reasoning of Massive Knowledge Bases
Xiang Ren, Nanyun Peng, William Yang Wang · 2018
In today's information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), and grows at an astonishing rate.How to turn such massive and unstructured text data into structured, actionable knowledge for computational machines, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community.