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Ontology based recommender system using social network data.

Mohamad Arafeh1, Paolo Ceravolo1, Azzam Mourad2

  • 1Computer Science Department, Università degli Studi di Milano, Milan, Italy.

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Summary
This summary is machine-generated.

This study introduces a new framework for sampling Online Social Networks (OSN) to improve data mining efficiency. The approach uses domain knowledge and ontologies to enhance recall and reduce time and cost for real-time decision-making.

Keywords:
Big dataData analysisData minerData samplingOntologyRecommender systemSocial network

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Online Social Networks (OSN) are crucial for real-time information but face challenges in data mining efficiency.
  • Current mining procedures are time-consuming and yield limited information.

Purpose of the Study:

  • To propose a novel framework for sampling Online Social Networks (OSN).
  • To enhance the efficiency and effectiveness of social network data mining.

Main Methods:

  • Developed a framework utilizing domain knowledge for tailored sampling strategies.
  • Integrated an ontology-based filtering layer to evaluate node relatedness within the knowledge graph.

Main Results:

  • Demonstrated a decrease in budget and time required for mining.
  • Achieved an increase in recall for extracted information.
  • Showcased the framework's adaptability for user recommendation systems.

Conclusions:

  • The strategy definition step is critical for social miner performance.
  • Ontologies significantly enhance recommendation analysis within knowledge graphs.