We present an online multilingual system for event detection and comprehension from media feeds. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer the journalism Ws: who, what, when and where. The generated events populate VLX-Stories-an event ontology-transforming unstructured text data to a structured knowledge base representation. Our system exploits an external entity Knowledge Graph (VKG) to help populate VLX-Stories. At the same time, this external knowledge graph can also be extended with a Dynamic Entity Linking (DEL) module, which detects emerging entities (EE) on unstructured data. The system is currently deployed in production and used by media producers in the editorial process, providing real-time access to breaking news. Each month, VLX-Stories detects over 9000 events from over 4000 news feeds from seven different countries and in three different languages. At the same time, it detects over 1300 EE per month, which populate VKG.

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world's knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events. The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.