公开(公告)号：US20230289626A1

公开(公告)日：2023-09-14

申请号：US18183410

申请日：2023-03-14

Applicant: Google LLC

Inventor： Hanjun Dai ,  Dale Eric Schuurmans ,  Xinyun Chen ,  Dengyong Zhou ,  Bo Dai ,  Hongyu Ren

IPC: G06N5/022 ,  G06F16/2453

CPC classification number: G06N5/022 ,  G06F16/2453

Abstract: Provided are computing systems, methods, and platforms for negative sampling in knowledge graphs with improved efficiency. A knowledge graph comprising entities and links between the entities can be obtained. A query computation graph comprising nodes and edges can be generated based on the knowledge graph. The nodes of the query computation graph can include anchor nodes, a root node, and intermediate nodes positioned in paths between the anchor nodes and the root node. A node cut of a query of the query computation graph can be determined and can include at least one node that cuts at least one path between each anchor node and the root node of the query computation graph. Negative samples can be identified by bidirectionally traversing the query computation graph in a first direction from the anchor nodes to the node cut and in a second direction from the root node to the node cut.

公开(公告)号：US11954442B2

公开(公告)日：2024-04-09

申请号：US16986534

申请日：2020-08-06

Applicant: Google LLC

Inventor： Chen Liang ,  Wei Yu ,  Quoc V. Le ,  Xinyun Chen ,  Dengyong Zhou

IPC: G06F40/30 ,  G06F16/33 ,  G06F40/20 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06F40/216 ,  G06F40/284

CPC classification number: G06F40/30 ,  G06F16/3347 ,  G06F40/20 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06F40/216 ,  G06F40/284

Abstract: The present disclosure is directed to systems and methods for performing reading comprehension with machine learning. More specifically, the present disclosure is directed to a Neural Symbolic Reader (example implementations of which may be referred to as NeRd), which includes a reader to encode the passage and question, and a programmer to generate a program for multi-step reasoning. By using operators like span selection, the program can be executed over a natural language text passage to generate an answer to a natural language text question. NeRd is domain-agnostic such that the same neural architecture works for different domains. Further, NeRd is compositional such that complex programs can be generated by compositionally applying the symbolic operators.