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Dynamic Multimodal Process Knowledge Graphs: A Neurosymbolic Framework for Compositional Reasoning
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AI
DOI：10.1109/MIC.2024.3520366.png)
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摘要
En 中文
Compositional reasoning, the cognitive process of breaking complex problems into manageable subproblems and recomposing them to generate new ideas, is fundamental to human problem solving and critical thinking. While deep learning models excel at pattern recognition, their capacity for true understanding and reasoning remains a topic of debate. Although the growth of the Internet has provided the necessary scale of data for model training, the way data is represented plays a pivotal role in enabling reasoning capabilities. This article introduces dynamic multimodal process knowledge graphs (DMPKGs), a novel neurosymbolic framework for data and knowledge representation that supports cognitive tasks such as compositional reasoning, high-level abstraction, explainability, and causal inference along with representation learning. The framework integrates data and knowledge into a unified, structured format enriched with semantics from multiple contexts. By prioritizing contextualized and semantically rich representations, DMPKGs aim to bridge the gap between pattern recognition and reasoning in artificial intelligence systems.
Keyword:
Training
Representation learning
Deep learning
Semantics
Knowledge graphs
Cognition
Data models
Pattern recognition
Internet
Problem-solving