Domain ontology to support open data analytics for aquaculture

João Pedro Abreu de Oliveira, Ruben M. Costa, José Lima, João Sarraipa, Ricardo Jardim-Gonçalves

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Aquaculture industry, which comprises mainly of SME companies, represents a significant source of protein for people. From an IT perspective, aquaculture is characterized high volumes of heterogeneous data, and also lack of interoperability intra and inter-organisations. Each organization uses different data representations, using its native languages and legacy classification systems to manage and organize information. The lack of semantic interoperability that exists can be minimized, if innovative semantic techniques for representing, indexing and searching sources of non-structured information are applied. The work presented here, describes the achievements under AQUASMART EU project, which aims to accelerate innovation in Europe's aquaculture through technology transfer for the deployment of an open data solution through multilingual data collection and analytics solutions and services, turning the large volumes of heterogeneous aquaculture data that is distributed across the value chain, into an open cloud of semantically interoperable data assets and knowledge. Results achieved so far do not address the final conclusions of the project but form the basis for the formalization of the AQUASMART semantic referential.

Original languageEnglish
Title of host publicationKEOD
PublisherSciTePress
Pages344-351
Number of pages8
Volume2
ISBN (Electronic)978-989758158-8
DOIs
Publication statusPublished - 2015
Event7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015

Conference

Conference7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
CountryPortugal
CityLisbon
Period12/11/1514/11/15

Keywords

  • Education and Training
  • Knowledge Representation
  • Multilingual
  • Ontology Engineering

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