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Detalhes

Detalhes

  • Nome

    Nuno Silva
  • Cargo

    Investigador Sénior
  • Desde

    17 julho 2023
Publicações

2020

DSS-Based Ontology Alignment in Solid Reference System Configuration

Autores
Gouveia, A; Maio, P; Silva, N; Lopes, R;

Publicação
Advances in Intelligent Systems and Computing

Abstract
uebe.Q is a managing software for solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive and solid business logic with a long and successful record of deployments. A recent business model change imposed that the evolution and configuration of the software, shifts from the company (and especially the development team) to consultants and other business partners, along with the fact that different systems and respective data/information need to be integrated with minimal intervention of the development team. The so far acceptable rigidity, fragility, immobility and opacity of the software became a problem. Especially, the system was prepared to deal with a specific database respecting a specific schema and code-defined semantics. This paper describes the approach taken to overcome the problems derived form the previous architecture, by adopting (i) ontologies for the specification of business concepts and (ii) an information-integration Decision Support System (DSS) for mapping the domain specific ontologies to the database schemas. © 2020, Springer Nature Switzerland AG.

2018

DSL-based configuration of solid referential management system: A case study

Autores
Figueiredo, E; Maio, P; Silva, N; Lopes, R;

Publicação
Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

Abstract
For the last decade, uebe.Q is being adopted by companies in different business areas and countries for managing compliance with solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive, solid and valuable business logic. When it is deployed for/on a company, it usually demands an extensive and specific adaptation (i.e. software refinement) and configuration process involving DigitalWind's ISO 9000 and ISO 1400 experts as well as software development and operation teams. However, a recent business model change imposed that the evolution and configuration of the software, shifts from DigitalWind (and especially from the development team) to external consultants and to other business partners, along with the fact that different third-party's systems and respective data/information need to be integrated with minimal intervention of the development team. This paper presents and overview of the re-engineering process taken to handle this business model change by adopting (i) ontologies for the specification of business concepts, (ii) closed-world assumption (CWA) rules for the specification of the dynamics of the system and (iii) Domain Specific Language (DSL) for the configuration of the system by domain/business experts. The DSL approach is further described in detail. © 2018 IEEE.

2017

A rule-based DSS for transforming Automatically-generated Alignments into Information Integration Alignments

Autores
Gouveia, A; Silva, N; Martins, P;

Publicação
19TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES (IIWAS2017)

Abstract
Analysis of automatically-generated alignments shows that ambiguous situations are quite common, preventing their application in scenarios demanding high quality and completeness, such ontology mediation (e.g. data transformation and information/knowledge integration). Even the best-performing alignment needs to be manually corrected, completed and verified before application. In this paper, we propose a decision support system (DSS) based in a general-purpose rule engine that assists the expert on improving and completing the automatically-generated alignments into fully-fledged alignments, balancing the precision and recall of the system with the user participation in the process. For that, the rules capture the preconditions (existing facts) and the actions to solve each (ambiguous) alignment scenario, in which the expert decision will be adopted in further automatic decisions. The evaluation of the proposed DSS shows the gain in reducing the need for expert's decisions while increasing the accuracy of the alignments. © 2017 Copyright is held by the owner/author(s).

2016

An unsupervised classification process for large datasets using web reasoning

Autores
Peixoto, R; Hassan, T; Cruz, C; Bertaux, A; Silva, N;

Publicação
Proceedings of the ACM SIGMOD International Conference on Management of Data

Abstract
Determining valuable data among large volumes of data is one of the main challenges in Big Data. We aim to extract knowledge from these sources using a Hierarchical Multi-Label Classification process called Semantic HMC. This process automatically learns a label hierarchy and classifies items from very large data sources. Five steps compose the Semantic HMC process: Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically the label hierarchy from data sources. The last two steps classify new items according to the label hierarchy. This paper focuses in the last two steps and presents a new highly scalable process to classify items from huge sets of unstructured text by using ontologies and rule-based reasoning. The process is implemented in a scalable and distributed platform to process Big Data and some results are discussed. © 2016 ACM.

2015

Semantically Enhancing Recommender Systems

Autores
Bettencourt, Nuno; Silva, Nuno; Barroso, Joao;

Publicação
Knowledge Discovery, Knowledge Engineering and Knowledge Management - 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknownunknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources. © Springer International Publishing AG 2016.

Teses
supervisionadas

2023

Estratégia Multi-Região e Recuperação de Desastres

Autor
RAFAEL BARBOSA FERREIRA

Instituição
IPP-ISEP

2023

Customer driven Performance & Availability Analysis for Warehouse Automation Industry

Autor
JOSÉ DIOGO CUNHA MACHADO MARTINS

Instituição
IPP-ISEP

2023

Desenvolvimento de solução de Telemetria (Status Monitor)

Autor
DIOGO FILIPE FREITAS RIBEIRO

Instituição
IPP-ISEP