Misinformation has a pervasive thread running through society, causing confusion, mistrust, and uncertainty. The detection, tracking, and countering of misinformation is a very active research area with an intense need for data about circulating claims and their attributes, fact-checks, and verification outcomes. Although various relevant datasets exist, they tend to be of limited scope in terms of time coverage, topics, country, language, and quantity. In this paper, we introduce CimpleKG as an open and continuously updated semantic resource. CimpleKG links daily updated data from 77 fact-checking organisations with over 217k documents from static misinformation datasets. The knowledge graph is also augmented with relevant textual features and entities extracted from the textual data integrated into the graph. At the time of writing, the knowledge graph contains more than 15m triples, including 263k+ distinct entities and 1m textual features with over 203k fact-checked claims, spanning 26 languages and 36 countries. CimpleKG is publicly available and has been used in various research studies and web applications. Resource Type: Knowledge Graph. License: CC BY-NC-SA 4.0. SPARQL Endpoint: https://purl.org/net/cimplekg/sparql . KG Releases: https://purl.org/net/cimplekg/knowledge-graph . KG Explorer: https://purl.org/net/cimplekg/explorer .

This paper summarises work where we combined semantic web technologies with deep learning systems to obtain state-of-the art explainable misinformation detection. We proposed a conceptual and computational model to describe a wide range of misinformation detection systems based around the concepts of credibility and reviews. We described how Credibility Reviews (CRs) can be used to build networks of distributed bots that collaborate for misinformation detection which we evaluated by building a prototype based on publicly available datasets and deep learning models.