Signals are emerging pieces of information relevant to a given context and offer potential for strategic advantage in a multitude of domains. However, sorting the signal from noise on large textual data is a very tedious process for humans. We introduce a scalable approach that extracts signals from hundreds of crawled sources and maps their metadata to a knowledge graph by exploiting state-of-the-art neural models for natural language understanding.