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Triplétoile: Extraction of knowledge from microblogging text.

Vanni Zavarella1, Sergio Consoli2, Diego Reforgiato Recupero1

  • 1Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, Cagliari, 09121, Italy.

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Summary
This summary is machine-generated.

This study introduces an advanced information extraction pipeline for creating knowledge graphs from social media. The system effectively extracts open-domain entities and relations from micro-blogging posts, achieving high precision.

Keywords:
Hierarchical clusteringInformation extractionKnowledge graphsNamed entity recognitionSocial media analysisWord embeddings

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Area of Science:

  • Natural Language Processing
  • Data Science
  • Artificial Intelligence

Background:

  • Existing knowledge graph extraction methods struggle with open-domain text sources like social media.
  • Micro-blogging posts contain unique entities and relations not easily modeled by current pipelines.

Purpose of the Study:

  • To develop an enhanced information extraction pipeline for knowledge graph construction from micro-blogging data.
  • To address the challenge of modeling open-domain entities and relations in social media text.

Main Methods:

  • Leveraged dependency parsing for enhanced information extraction.
  • Employed unsupervised hierarchical clustering over word embeddings for relation classification.
  • Applied the pipeline to a corpus of 100,000 tweets on digital transformation.

Main Results:

  • Achieved over 95% precision in extracting semantic triples.
  • Outperformed similar pipelines by approximately 5% in precision.
  • Generated a higher number of triples compared to existing methods.

Conclusions:

  • The proposed pipeline effectively extracts knowledge graphs from social media.
  • The system demonstrates superior performance and efficiency for open-domain information extraction.
  • The generated knowledge graph and methodology are publicly released for further research.