May 10, 2021
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the world’s structured knowledge, and as a way to integrate information extracted from multiple data sources. Knowledge graphs have started to play a central role in representing the information extracted using natural language processing and computer vision. Domain knowledge expressed in KGs is being input into machine learning models to produce better predictions. Our goals in this blog post are to (a) explain the basic terminology, concepts, and usage of KGs, (b) highlight recent applications of KGs that have led to a surge in their popularity, and (c) situate KGs in the overall landscape of AI. This blog post is a good starting point before reading a more extensive survey or following research seminars on this topic.
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Researchers at the University of Maryland, Baltimore County (UMBC) have made strides in automated legal document analytics (ALDA) by creating a way to machine-process the Code of Federal Regulations (CFR). The CFR is a complex document containing policies related to doing business with the federal government. All business affiliates of the federal government must comply with the CFR. For government contracts to be equitably open to a broad range of businesses, policies within the CFR must be accessible.
This document automation is just one part of a broader project to help contractors and other entities manage and monitor their legal documents. Directed by Karuna Joshi, associate professor of information systems, the team has successfully managed to do a complete analysis of the CFR.