Temporal reasoning in a logic programming language with modularity

Actualmente os Sistemas de Informação Organizacionais (SIO) lidam cada vez mais com informação que tem dependências temporais. Neste trabalho concebemos um ambiente de trabalho para construir e manter SIO Temporais. Este ambiente assenta sobre um linguagem lógica denominada Temporal Contextua) Logic Programming que integra modularidade com raciocínio temporal fazendo com que a utilização de um módulo dependa do tempo do contexto. Esta linguagem é a evolução de uma outra, também introduzida nesta tese, que combina Contextua) Logic Programming com Temporal Annotated Constraint Logic Programming, na qual a modularidade e o tempo são características ortogonais. Ambas as linguagens são formalmente discutidas e exemplificadas. As principais contribuições do trabalho descrito nesta tese incluem: • Optimização de Contextua) Logic Programming (CxLP) através de interpretação abstracta. • Sintaxe e semântica operacional para uma linguagem que combina de um modo independente as linguagens Temporal Annotated Constraint Logic Programming (TACLP) e CxLP. É apresentado um compilador para esta linguagem. • Linguagem (sintaxe e semântica) que integra de um modo inovador modularidade (CxLP) com raciocínio temporal (TACLP). Nesta linguagem a utilização de um dado módulo está dependente do tempo do contexto. É descrito um interpretador e um compilador para esta linguagem. • Ambiente de trabalho para construir e fazer a manutenção de SIO Temporais. Assenta sobre uma especificação revista da linguagem ISCO, adicionando classes e manipulação de dados temporais. É fornecido um compilador em que a linguagem resultante é a descrita no item anterior. ABSTRACT- Current Organisational Information Systems (OIS) deal with more and more Infor-mation that, is time dependent. In this work we provide a framework to construct and maintain Temporal OIS. This framework builds upon a logical language called Temporal Contextual. Logic Programming that deeply integrates modularity with tem-poral reasoning making the usage of a module time dependent. This language is an evolution of another one, also introduced in this thesis, that combines Contextual Logic Programming with Temporal Annotated Constraint Logic Programming where modularity and time are orthogonal features. Both languages are formally discussed and illustrated. The main contributions of the work described in this thesis include: • Optimisation of Contextual Logic Programming (CxLP) through abstract interpretation. • Syntax and operational semantics for an independent combination of the temporal framework Temporal Annotated Constraint Logic Programming (TACLP) and CxLP. A compiler for this language is also provided. • Language (syntax and semantics) that integrates in a innovative way modularity (CxLP) with temporal reasoning (TACLP). In this language the usage of a given module depends of the time of the context. An interpreter and a compiler for this language are described. • Framework to construct and maintain Temporal Organisational Information Systems. It builds upon a revised specification of the language ISCO, adding temporal classes and temporal data manipulation. A compiler targeting the language presented in the previous item is also given

Knowledge Representation of Crime-Related Events: a Preliminary Approach

The crime is spread in every daily newspaper, and particularly on criminal investigation reports produced by several Police departments, creating an amount of data to be processed by Humans. Other research studies related to relation extraction (a branch of information retrieval) in Portuguese arisen along the years, but with few extracted relations and several computer methods approaches, that could be improved by recent features, to achieve better performance results. This paper aims to present the ongoing work related to SEM (Simple Event Model) ontology population with instances retrieved from crime-related documents, supported by an SVO (Subject, Verb, Object) algorithm using hand-crafted rules to extract events, achieving a performance measure of 0.86 (F-Measure)

Modularity and Temporal Reasoning: a Logic Programming Approach

Albeit temporal reasoning and modularity are very prolific fields of research in Logic Programming (LP), we find few examples of their integration. In this paper we propose the addition of temporal annotations to a modular extension of LP. Moreover, besides an illustrative example we also provide a sketch for a compiler, allowing this way for the development of applications based such language

Temporal Annotations for a Contextual Logic Programming Language}

In this paper we propose the combination of modularity and temporal reasoning using logic programming as common ground. Moreover, we consider that the usage of a given module is influenced by temporal constraints, i.e. modularity and temporal reasoning are strongly connected. Besides illustrative examples, we also present the operational semantics and corresponding compiler for this language

Integrating Temporal Annotations in a Modular Logic Language

Albeit temporal reasoning and modularity are very prolific fields of research in Logic Programming (LP) we find few examples of their integration. Moreover, in those examples, time and modularity are considered orthogonal to each other. In this paper we propose the addition of temporal annotations to a modular extension of LP such that the usage of a module is influenced by temporal conditions. Besides illustrative examples we also provide an operational semantics together with a compiler, allowing this way for the development of applications based on such language

An Overview of IoT and Healthcare

This paper is an overview of some of the implications of IoT on the healthcare field. Due to the increasing of IoT solutions, healthcare cannot be outside of this paradigm. The contribution of this paper is to introduce directions to achieve a global connectivity between the Internet of Things (IoT) and the medical environments. The need to integrate all in a global environment is a huge challenge to all (from electrical engineers to data engineers).This revolution is redesigning the way we see healthcare, from the smallest sensor to the big data collected

NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval

This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT