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Human-computer interaction based on background knowledge and emotion certainty.

Qiang He1

  • 1State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a knowledge graph ripple network model for more natural human-computer interaction. Robots demonstrate improved response accuracy and emotional friendliness by using background knowledge and emotional measurements.

Keywords:
Background knowledgeEmotion certaintyHuman-computer interactionKnowledge graph ripple network

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Current human-computer interaction systems lack background knowledge, leading to inconsistent robot responses.
  • Simulating natural human communication is key to developing more intelligent interaction systems.

Purpose of the Study:

  • To propose a novel human-computer interaction model using a knowledge graph ripple network.
  • To enhance robot response naturalness, intelligence, and consistency.
  • To improve affective and content friendliness in human-robot interactions.

Main Methods:

  • Developed a human-computer interaction model integrating a knowledge graph ripple network.
  • Calculated affective evaluation and emotional measurement for human-computer interaction friendliness.
  • Embedded conversational entities into the knowledge graph ripple network for interest identification.
  • Enabled robot responses based on emotional and content friendliness.

Main Results:

  • The proposed model significantly improved emotional friendliness and coherence in robot responses.
  • Response accuracy in human-computer interaction was enhanced by at least 5.5% compared to existing models.
  • The integration of background knowledge and emotional measurement proved effective.

Conclusions:

  • The knowledge graph ripple network model offers a more natural and intelligent approach to human-computer interaction.
  • Incorporating affective evaluation and external knowledge enhances robot communication capabilities.
  • The model effectively addresses limitations of current systems by providing context-aware and emotionally intelligent responses.