The Path to Autonomous Learners
This addresses the challenge of autonomous knowledge acquisition for AI systems, but it appears incremental as it builds on existing methods like knowledge graphs and neural networks.
The paper tackles the problem of enabling intelligent systems to acquire domain knowledge autonomously by proposing a hybrid model that combines an upper ontology, knowledge graph database, and Logic Neural Network, resulting in a system capable of enriching and extending its knowledge to new domains.
In this paper, we present a new theoretical approach for enabling domain knowledge acquisition by intelligent systems. We introduce a hybrid model that starts with minimal input knowledge in the form of an upper ontology of concepts, stores and reasons over this knowledge through a knowledge graph database and learns new information through a Logic Neural Network. We study the behavior of this architecture when handling new data and show that the final system is capable of enriching its current knowledge as well as extending it to new domains.