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A Framework for Fostering Easier Access to Enriched Textual Information

Authors Gabriel Silva , Mário Rodrigues , António Teixeira , Marlene Amorim

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Author Details

Gabriel Silva
  • IEETA, DETI, University of Aveiro, Portugal
  • LASI – Intelligent System Associate Laboratory, Coimbra, Portugal
Mário Rodrigues
  • IEETA, ESTGA, University of Aveiro, Portugal
  • LASI – Intelligent System Associate Laboratory, Coimbra, Portugal
António Teixeira
  • IEETA, DETI, University of Aveiro, Portugal
  • LASI – Intelligent System Associate Laboratory, Coimbra, Portugal
Marlene Amorim
  • GOVCOPP, DEGEIT, University of Aveiro, Portugal

Funding

This research is funded by National Funds through the FCT - Foundation for Science and Technology (UI/BD/153571/2022). It is also funded, Research Unit IEETA, by National Funds through the FCT - Foundation for Science and Technology, in the context of the project UIDB/00127/2020.

Cite AsGet BibTex

Gabriel Silva, Mário Rodrigues, António Teixeira, and Marlene Amorim. A Framework for Fostering Easier Access to Enriched Textual Information. In 12th Symposium on Languages, Applications and Technologies (SLATE 2023). Open Access Series in Informatics (OASIcs), Volume 113, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.SLATE.2023.2

Abstract

Considering the amount of information in unstructured data it is necessary to have suitable methods to extract information from it. Most of these methods have their own output making it difficult and costly to merge and share this information as there currently is no unified way of representing this information. While most of these methods rely on JSON or XML there has been a push to serialize these into RDF compliant formats due to their flexiblity and the existing ecosystem surrounding them. In this paper we introduce a framework whose goal is to provide a serialization of enriched data into an RDF format, following FAIR principles, making it more interpretable, interoperable and shareable. We process a subset of the WikiNER dataset and showcase two examples of using this framework: One using CoNLL annotations and the other by performing entity-linking on an already existing graph. The results are a graph with every connection starting from the document and finishing on tokens while keeping the original text intact while embedding the enriched data into it, in this case the CoNLL annotations and Entities.

Subject Classification

ACM Subject Classification
  • Information systems → Document representation
  • Information systems → Ontologies
Keywords
  • Knowledge graphs
  • Enriched data
  • Natural language processing
  • Triplestore

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Supplementary Materials

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