In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale

We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames, i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.