Vadalog: Overview, Extensions and Business Applications

Knowledge graphs (KGs) have in recent years gained a large momentum both in academic research and in business applications. They have become a bridge between databases, artificial intelligence (AI), data science, the (semantic) web, linked data, and many other areas. In particular, in declarative AI, they have become a bridge between logic-based reasoning, and machine learning-based reasoning. Languages for KGs on the one hand, and systems for KGs – i.e., Knowledge Graph Managament System (KGMS) – on the other hand, have garnered increasing attention. Of particular importance are language and system extensions – such as probabilistic reasoning, numeric reasoning, etc. – supporting various real-world applications, and the business applications that can be built using such extensions. In this work, we give an overview of the Vadalog language and system, a KGMS. We focus on three areas: (1) a basic overview, including an introduction to dependencies, the Datalog and Vadalog languages, (2) the extensions of the system, including arithmetic and aggregation, real-world data interfaces, temporal reasoning, and machine learning, and (3) the business applications, including: corporate governance, media intelligence, supply chains, collateral eligibility, hostile takeovers, smart anonymization, and anti-money laundering.