The Vadalog system: datalog-based reasoning for knowledge graphs

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation

The Vadalog system: datalog-based reasoning for knowledge graphs

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation

EXLEngine: executable schema mappings for statistical data processing

Data processing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models such as time series. We illustrate here an experience at the Bank of Italy where (i) a language, EXL, has been defined for the declarative specification of statistical programs, (ii) an approach for the translation of EXL code into executables in various target systems has been developed, and (iii) a concrete implementation, EXLEngine, has been carried out. The approach leverages on schema mappings as an intermediate specification step, in order to facilitate the translation from EXL towards several target systems

Eliminating Harmful Joins in Warded Datalog+/−

We provide a rewriting technique of Warded Datalog+/− settings to sustain decidability and data tractability of reasoning tasks in the presence of existential quantification and recursion. To achieve this behaviour in practice, reasoners implement specialized strategies which exploit the theoretical bases of the language to control the effects of recursion, ensuring reasoning termination with small memory footprint. However, as a necessary condition for such exploitation, the setting is required to be in a “normalized form”, essentially without joins on variables affected by existential quantification. We present the Harmful Join Elimination, a normalization algorithm of Warded Datalog+/− that removes such “harmful” joins, supporting the tractability of the reasoning task as well as the full expressive power of the language. The algorithm is integrated in the Vadalog system, a Warded Datalog+/− -based reasoner that performs ontological reasoning in complex scenarios

Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and Reasoning

Vadalog is a logic-based reasoning language for modern AI solutions, in particular for Knowledge Graph (KG) systems. It is showing very effective applicability in the financial realm, with success stories in a vast range of scenarios, including: creditworthiness evaluation, analysis of company ownership and control, prevention of potential takeovers of strategic companies, prediction of hidden links between economic entities, detection of family businesses, smart anonymization of financial data, fraud detection and anti-money laundering. In this work, we first focus on the language itself, giving a self-contained and accessible introduction to Warded Datalog+/-, the formalism at the core of Vadalog, as well as to the Vadalog system, a state-of-the-art KG system. We show the essentials of logic-based reasoning in KGs and touch on recent advances where logical inference works in conjunction with the inductive methods of machine learning and data mining. Leveraging our experience with KGs in Banca d’Italia, we then focus on some relevant financial applications and explain how KGs enable the development of novel solutions, able to combine the knowledge mined from the data with the domain awareness of the business experts