TY - GEN
T1 - Vadalog
T2 - 18th Reasoning Web Summer School, RW 2022
AU - Baldazzi, Teodoro
AU - Bellomarini, Luigi
AU - Gerschberger, Markus
AU - Jami, Aditya
AU - Magnanimi, Davide
AU - Nissl, Markus
AU - Pavlović, Aleksandar
AU - Sallinger, Emanuel
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85161480338
U2 - 10.1007/978-3-031-31414-8_5
DO - 10.1007/978-3-031-31414-8_5
M3 - Conference contribution
AN - SCOPUS:85161480338
SN - 9783031314131
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 198
BT - Reasoning Web. Causality, Explanations and Declarative Knowledge - 18th International Summer School 2022, Tutorial Lectures
A2 - Bertossi, Leopoldo
A2 - Xiao, Guohui
PB - Springer
Y2 - 27 September 2022 through 30 September 2022
ER -