Tue 18 May 2021

An Introduction to Knowledge Graphs

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.

Source: An Introduction to Knowledge Graphs, an article by Vinay K. Chaudhri, Naren Chittar, and Michael Genesereth.

Coverage-guided Test Data Selection

Getting your hands on real-world data to test your software is the real deal. Nothing comes closer to reality than feeding an application with actual data from the wild. But what if the amount of data is more than you can handle? There are limitations on how much time you can spend on test runs. Whether you are running tests on a developer machine or as part of a Continuous Integration system, you probably won't be able to crunch large amounts of data each time you make a code change. Sooner or later, you will be forced to shrink your test corpus to a more manageable size. This article presents an approach that uses code coverage metrics to determine a representative test data subset.

Source: Coverage-guided Test Data Selection, an article by Michael Seifert.