Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning

2025-01-22

AI Summary

Automated identification and dynamic tracking of technological trends in quantum communication—particularly cryptographic protocols and security mechanisms—remain challenging due to the field’s rapid evolution and domain-specific complexity. Method: This paper proposes a knowledge-enhanced trend analysis framework integrating expert knowledge with deep reinforcement learning. It introduces an expert-weighted topic modeling mechanism that injects domain expertise into LDA/Contextualized Topic Models (CTM); designs an entropy-aware reward function balancing topic divergence (KL divergence) and temporal similarity dynamics to guide Deep Q-Network (DQN) optimization for trend ranking; and establishes an interpretable, verifiable multi-temporal evolutionary tracking pipeline. Results: Evaluated on real-world scholarly literature, the method accurately identifies and ranks key technological trends, achieving high agreement with domain experts (Cohen’s κ > 0.85). It enables proactive trend alerting and cross-cycle evolutionary modeling, demonstrating both validity and practical utility for strategic R&D foresight in quantum security.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Topic discovery and tracking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This study presents a method for exploring advancements in a specific technological domain. It combines topic modeling, expert input, and reinforcement learning (RL). The proposed approach has three key steps: (1) generate aspect-based topic models using expert-weighted keywords to emphasize critical aspects, (2) analyze similarities and entropy changes by comparing topic distributions across iterative models, and (3) refine topic selection using reinforcement learning (RL) with a modified reward function that integrates changes in topic divergence and similarity across iterations. The method is tested on quantum communication documents with a focus on advances in cryptography and security protocols. The results show the method's effectiveness and can identify, rank, and track trends that match expert input. The framework provides a robust tool for exploring evolving technological landscapes.

Problem

• Quantum Communication
• Cryptography
• Security Protocols

Innovation

• Thematic Reinforcement Learning
• Quantum Communication Analysis
• Expert Knowledge Integration