MLres

5 ms·

1. 1.▲
   by 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 lear
2. 2.▲
   by MLres 7y ago

   Capability vs practical rule injection: The question is that if the NNs are capable of encoding logical rules? The paper proposes a neural network and proves that the network is capable of expressing any ground truth over encoded rules in t
3. 3.▲
   by MLres 7y ago

   From Logic to Algebra: After embedding a KG in its corresponding vector space, each symbolic elements (entities and relations) will be assigned a vector. Moving from symbols to vectors is equivalent with moving from logic to algebra. In oth
4. 4.▲
   by MLres 7y ago

   Reasoning by Learning:When the Neural Network maps the KG to a vector space and learns the KG representation, it can do reasoning: the NN gets a triple (e.g., (Bonn, IsAcityIn, Germany)) and determines if this is true or not. This predictio
5. 5.▲
   by MLres 7y ago

   Neural Based Embedding: Neural Networks (NNs) have been widely used to learn over data represented in the form of vectors. NNs can be used to learn over symbolic connected data (e.g., KG). They can be used to learn knowledge graph represent
6. 6.▲
   by MLres 7y ago

   Learning over Symbolic Data: Knowledge graphs mostly contain symbolic data which are connected. Learning over such a data is indeed a challenging task in Machine Learning (ML) where ML models mostly work on vectors. Knowledge Graph Embeddi
7. 7.▲
   by MLres 7y ago

   Knowledge Representation: Knowledge has played a pivotal role in the development of civilizations. The 21st century has become the century of explosion of humans knowledge thanks to the development of sophisticated communication systems. Th