Luigi Bellomarini

Luigi Bellomarini
Banca d'Italia · IT Research Unit

PhD in Computer Science

About

41
Publications
5,589
Reads
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291
Citations
Introduction
Software Engineer and Researcher at the IT Department of Banca d'Italia since 2010. My research interests include database theory, data models, schema and data translation, data integration and data exchange, artificial intelligence, in particular logic-based methods of AI, and knowledge graphs.
Additional affiliations
April 2010 - present
Banca d'Italia
Position
  • Software Engineer

Publications

Publications (41)
Preprint
Datalog^E is the extension of Datalog with existential quantification. While its high expressive power, underpinned by a simple syntax and the support for full recursion, renders it particularly suitable for modern applications on knowledge graphs, query answering (QA) over such language is known to be undecidable in general. For this reason, diffe...
Article
Following the recent successful examples of large technology companies, many modern enterprises seek to build Knowledge Graphs to provide a unified view of corporate knowledge, and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typica...
Preprint
In this paper, we investigate finite representations of DatalogMTL. First, we introduce programs that have finite models and propose a toolkit for structuring the execution of DatalogMTL rules into sequential phases. Then, we study infinite models that eventually become constant and introduce sufficient criteria for programs that allow for such rep...
Preprint
Full-text available
Ontological query answering is the problem of answering queries in the presence of schema constraints representing the domain of interest. Datalog+/- is a common family of languages for schema constraints, including tuple-generating dependencies (TGDs) and equality-generating dependencies (EGDs). The interplay of TGDs and EGDs leads to undecidabili...
Preprint
Recent years have seen increasing popularity of logic-based reasoning systems, with research and industrial interest as well as many flourishing applications in the area of Knowledge Graphs. Despite that, one can observe a substantial lack of specific tools able to generate nontrivial reasoning settings and benchmark scenarios. As a consequence, ev...
Chapter
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 takeover...
Chapter
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 r...
Chapter
We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), able to satisfy the requirements of ontological reasoning: full recursion, powerful existential quantification, and the ability to express inductive definitions. Vadalog is based on Warded Datalog+/−, an existential rule language that strikes a good balance...
Chapter
Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies. Many definitions exist of what a Knowledge Graph is, and in this chapter, we are going to take the position that precisely in the multitude of definitions lies one of the strengths of the area. We will choose a particular perspective, which we will call the layere...
Chapter
In this chapter we introduce the aspect of reasoning in Knowledge Graphs. As in Chap. 2, we will give a broad overview focusing on the multitude of reasoning techniques: spanning logic-based reasoning, embedding-based reasoning, neural network-based reasoning, etc. In particular, we will discuss three dimensions of reasoning in Knowledge Graphs. Co...
Conference Paper
Full-text available
Money laundering is a major threat to the good functioning of financial systems. Despite huge technological investments, with machine learning at the heart of the Fintech revolution, we are still lacking explainable solutions in fighting money laundering, especially for Financial Intelligence Units (FIUs). This paper is based on the joint commitmen...
Conference Paper
We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), able to satisfy the requirements of ontological reasoning: full recursion, powerful existential quantification, and the ability to express inductive definitions. Vadalog is based on Warded Datalog +/-, an existential rule language that strikes a good balance...
Article
The introduction of novel Datalog +/- fragments with good theoretical properties, together with the growing use of enterprise knowledge graphs motivated the development of Vadalog, a knowledge graph management system developed at the University of Oxford. It adopts Warded Datalog +/- as the core of its language for knowledge representation and reas...
Preprint
Full-text available
In the COVID-19 outbreak, governments have applied progressive restrictions to production activities, permitting only those that are considered strategic or that provide essential services. This is particularly apparent in countries that have been stricken hard by the virus, with Italy being a major example. Yet we know that companies are not just...
Technical Report
Full-text available
In the COVID-19 outbreak, governments have applied progressive restrictions to production activities, permitting only those that are considered strategic or that provide essential services. This is particularly apparent in countries that have been stricken hard by the virus, with Italy being a major example. Yet we know that companies are not just...
Article
Full-text available
Background Data scientists spend considerable amounts of time preparing data for analysis. Data preparation is labour intensive because the data scientist typically takes fine grained control over each aspect of each step in the process, motivating the development of techniques that seek to reduce this burden. Results This paper presents an archit...
Article
The definition of mappings between heterogeneous schemas is a critical activity of any database application. Existing tools provide high level interfaces for the discovery of correspondences between elements of schemas, but schema mappings need to be manually specified every time from scratch, even if the scenario at hand is similar to one that has...
Chapter
With the introduction of its Knowledge Graph [20], Google has coined the name for a new generation of knowledge-based systems that go beyond what was previously expected of areas that include graph databases, knowledge bases, machine-learning systems and rule-based logical reasoners.
Preprint
Full-text available
Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typical...
Preprint
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 knowl\-edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quan...
Article
Full-text available
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. We illustrate here a proposal where a high-level language, EXL, is used for the declarative specification of statistical programs, and a...
Article
Full-text available
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 knowl- edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quan...
Conference Paper
Full-text available
Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve...
Technical Report
Full-text available
The SDMX Technical Working Group is pleased to present the version 1.0 of the Validation and Transformation Language, in short VTL. The work on VTL was launched at the end of 2012 by the SDMX Secretariat. SDMX already has a package for transformations and expressions which is present in the information model, although a specific language does not y...
Conference Paper
Full-text available
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...
Article
To support heterogeneity is a major requirement in current approaches to integration and transformation of data. This paper proposes a new approach to the translation of schema and data from one data model to another, and we illustrate its implementation in the tool MIDST-RT.We leverage on our previous work on MIDST, a platform conceived to perform...
Article
Full-text available
Model management is a metadata-based approach to database problems aimed at supporting the productivity of developers by providing schema manipulation operators. Here we propose MISM (Model Independent Schema Management), a platform for model management offering a set of operators to manipulate schemas, in a manner that is both model-independent (i...
Conference Paper
Full-text available
A runtime approach to model-generic translation of schema and data is proposed. It is based on our previous work on MIDST, a platform conceived to perform translations in an ofi-line fashion. In the original approach, the source database is imported into a dictionary, where it is stored according to a universal model. Then, the translation is appli...
Conference Paper
Full-text available
Model management addresses problems dealing with forms of collaboration among heterogeneous databases. This collaboration may include exchange of data, schema integration, synchronization, translation and, in general, any issue characterized by a data evolving scenario. It provides a structured framework allowing standard solutions to data programm...
Conference Paper
Full-text available
Model management,is a metadata-based approach to data- base problems aimed,at supporting the productivity of developers by providing schema manipulation operators. Here we propose MISM (Model Independent Schema Management), a platform for model management,ofiering a set of operators to manipu- late schemas, in a manner that is both model-independen...
Conference Paper
Full-text available
Time series data are becoming more and more pervasive in many business and human endeavors. The challenge of developing dedicated systems for managing such data lies in handling the problem of the scale: time series are inherently growing and so the algorithms to compute answers to queries soon become ineffective. In this paper, we describe Samizda...

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