4 ms·

Thanks for linking this paper. I have professional experience working with both knowledge graphs (as a contractor at Google) and deep learning (Capital One). Ad

Thanks for linking this paper. I have professional experience working with both knowledge graphs (as a contractor at Google) and deep learning (Capital One). Adding logical rules as prior knowledge (an analog might be structure in our brains that we are born with, not learned) with KG embedding is a really interesting idea.

MLres 7y ago
Thanks for the comment, Developing a learning model which is more close to the learning done by human brain is indeed an interesting problem. That there might be a prior knowledge in brain which is not learned, but affects (guides) the learning process, inference and reasoning is really interesting. The intuition is that the real world is a big multi-modal knowledge graph with an underlying ontology, axioms and rules (e.g., principals in classic mechanics etc). With this intuition, humans learn new facts from the big real world multi modal knowledge graph by incorporating their (Aristotelian) senses as well as five (inward/outward) wits. One prior knowledge might be intrinsic. The nice point is that AI experts can map the knowledge graph and its elements (axioms, rules, symbols, facts etc) to a target vector space and find equivalent of the elements for the vector space (e.g., logical formula are mapped to their corresponding equivalent algebraic formula). Actually, this is multidisciplinary topic and I would invite people from different backgrounds and fields to add comments. It would be appreciated