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Updated: Sep 6, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge Graph-Enabled Text-Based Automatic Personality Prediction.

Majid Ramezani1, Mohammad-Reza Feizi-Derakhshi1, Mohammad-Ali Balafar2

  • 1Computerized Intelligence Systems Laboratory, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

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Summary
This summary is machine-generated.

This study introduces a new knowledge graph method for Automatic Personality Prediction (APP) using text. The approach enhances personality prediction accuracy by leveraging enriched knowledge graphs and deep learning models.

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Area of Science:

  • Computational Linguistics
  • Psychology
  • Artificial Intelligence

Background:

  • Understanding personality is crucial for interpersonal interactions.
  • Online communication generates vast amounts of text data encoding personality traits.
  • Text-based Automatic Personality Prediction (APP) aims to infer personality from written communication.

Purpose of the Study:

  • To propose a novel knowledge graph-enabled approach for text-based Automatic Personality Prediction (APP).
  • To enhance personality prediction accuracy by integrating external knowledge resources.
  • To evaluate the effectiveness of deep learning models on embedded knowledge graph representations.

Main Methods:

  • Constructing a knowledge graph from input text by matching concepts with DBpedia.
  • Enriching the knowledge graph with DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database.
  • Embedding the enriched knowledge graph into a matrix representation.
  • Utilizing deep learning models including CNN, RNN, LSTM, and BiLSTM for personality prediction.

Main Results:

  • The knowledge graph-enhanced approach significantly improved prediction accuracies across all tested deep learning models.
  • Enriching the text representation with external knowledge sources proved beneficial for APP.
  • Deep learning architectures effectively processed the knowledge graph embeddings for personality forecasting.

Conclusions:

  • The proposed knowledge graph-enabled method offers a powerful enhancement for text-based Automatic Personality Prediction.
  • Integrating structured knowledge with text data improves the performance of personality prediction models.
  • This approach holds promise for more accurate and nuanced understanding of individual personalities through digital communication.