Search Results for author:
Found 3 papers, 1 papers with code
Neural-Symbolic Message Passing with Dynamic Pruning
However, they perform unsatisfactorily on negative queries and fail to address the noisy messages between variable nodes in the query graph.
Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering
To this end, we propose a neural one-point embedding method called Pathformer based on the tree-like computation graph, i. e., query computation tree.
Conditional Logical Message Passing Transformer for Complex Query Answering
In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
