Managing data through the lens of an ontology

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems. This article introduces the notion of ontology-based data management, illustrating the main ideas underlying the paradigm, and pointing out the importance of knowledge representation and automated reasoning for addressing the technical challenges it introduces

Ontology-based explanation of classifiers

The rise of data mining and machine learning use in many applications has brought new challenges related to classification. Here, we deal with the following challenge: how to interpret and understand the reason behind a classifier's prediction. Indeed, understanding the behaviour of a classifier is widely recognized as a very important task for wide and safe adoption of machine learning and data mining technologies, especially in high-risk domains, and in dealing with bias.We present a preliminary work on a proposal of using the Ontology-Based Data Management paradigm for explaining the behavior of a classifier in terms of the concepts and the relations that are meaningful in the domain that is relevant for the classifier

Semantic technologies for the production and publication of open data in ACI - Automobile club d’Italia

Semantic technologies combine knowledge representation techniques with artificial intelligence in order to achieve a more effective management of enterprise knowledge bases, thanks to the separation of the conceptual level of the applications from the logical and physical ones, and to the automatic reasoning services they deploy for data access and control. In this context, Ontology-based Data Management (OBDM) [3] has consolidated itself as a paradigm for data integration and governance, based on a three-tier architecture: the ontology, the data sources, and the mappings, which declaratively link the ontology predicates to the data in the sources. In this talk1 we present a joint project by Sapienza University of Rome, the Automobile Club d’Italia (ACI), and OKKAM S.r.l.2, a spinoff of the University of Trento. The objectives of the project were the definition of an ontology of ACI’s Public Vehicle Register (PRA) and car tax domains, the development of an OBDM system to access the data through such ontology, and the creation of a web portal for the publication of ACI’s car parc data in Linked Open format

A framework for explaining query answers in dl-lite

An Ontology-based Data Access system is constituted by an ontology, namely a description of the concepts and the relations in a domain of interest, a database storing facts about the domain, and a mapping between the data and the ontology. In this paper, we consider ontologies expressed in the popular DL-Lite family of Description Logic, and we address the problem of computing explanations for answers to queries in an OBDA system, where queries are either positive, in particular conjunctive queries, or negative, i.e., negation of conjunctive queries. We provide the following contributions: (i) we propose a formal, comprehensive framework of explaining query answers in OBDA systems based on DL-Lite; (ii) we present an algorithm that, given a tuple returned as an answer to a positive query, and given a weighting function, examines all the explanations of the answer, and chooses the best explanation according to such function; (iii) we do the same for the answers to negative queries. Notably, on the way to get the latter result, we present what appears to be the first algorithm that computes the answers to negative queries in DL-Lite

Two-dimensional rule language for querying sensor log data: a framework and use cases

Motivated by two industrial use cases that involve detecting events of interest in (asynchronous) time series from sensors in manufacturing rigs and gas turbines, we design an expressive rule language DslD equipped with interval aggregate functions (such as weighted average over a time interval), Allen’s interval relations and various metric constructs. We demonstrate how to model events in the uses cases in terms of DslD programs. We show that answering DslD queries in our use cases can be reduced to evaluating SQL queries. Our experiments with the use cases, carried out on the Apache Spark system, show that such SQL queries scale well on large real-world datasets

Estudos dos gastos da União para o enfrentamento da COVID-19 durante 2020

Trabalho de Conclusão de Curso (Graduação)Reconhecida no início de 2020 como pandemia pela Organização Mundial da Saúde (OMS), o surto de COVID-19 causou diversas vítimas ao redor do mundo, mas não apenas pela sua mortalidade, mas também pois as medidas para conter o avanço do vírus prejudica a economia global. Este trabalho busca entender como o Governo Federal do Brasil destinou as verbas públicas e se os estados mais atingidos receberam recursos além de entender a situação geral de cada estado analisado. Esse pesquisa utilizou de técnicas de Web Scraping, a linguagem Python e o data store Big Query para realizar a extração dos dados, já a análise foi realizada usando a mesma linguagem em um jupyter notebook e utilizando-se de gráficos para visualizar os dados. Verificou-se que a situação de alguns estados estava crítica mas que medidas foram tomadas, mas que medidas, por meio das Ações Orçamentárias foram tomadas. A partir desses resultados podemos concluir que, apesar de algumas falhas, a União conseguiu ajudar, de forma geral, os estados analisados no momento de necessidade

Managing Data through the Lens of an Ontology

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems. This article introduces the notion of ontology-based data management, illustrating the main ideas underlying the paradigm, and pointing out the importance of knowledge representation and automated reasoning for addressing the technical challenges it introduces