Patent ID: 11915161
Assignee: TSINGHUA UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. The method according to claim 1, wherein each of the individual operator networks comprises a data collating module and a data processing module, wherein the data processing module comprises a feature extracting network, a feature transforming network, a feature integrating network and a result outputting network;
wherein the step of acquiring the simulated samples, and pre-training the individual instances of the operator networks comprises:
pre-processing the acquired simulated samples that express the same relations between target nodes, to obtain sample triplet data marked in the graph data structure, wherein the sample triplet data comprise: a plurality of target sample nodes, attributes corresponding to the plurality of target sample nodes individually, and relations between the target sample nodes;
determining, by the data collating module, the processing data of any one head-tail node pair in the plurality of target sample nodes, wherein the processing data comprises an attribute of a sample head node, an attribute of a sample tail node, a neighbor relation of the sample head node, a neighbor relation of the sample tail node, a relation of the sample head node-the sample tail node, and a relation of the sample tail node-the sample head node;
extracting, by the feature extracting network, a head-tail node feature, a head-node-neighbor feature set, a tail-node-neighbor feature set, a feature set of a relation from a head node pointing to a tail node, and a feature set of a relation from a tail node pointing to a head node;
by the feature transforming network, according to the head-tail node feature, transforming the head-node-neighbor feature set, the tail-node-neighbor feature set, the feature set of the relation from the head node pointing to the tail node, and the feature set of the relation from the tail node pointing to the head node;
performing, by the feature integrating network, averaging and extending processing to an output of the feature transforming network;
by the result outputting network, according to an output of the feature integrating network, determining an outputted result of the individual operator network and marking in the graph data structure, wherein the outputted result comprises: a confidence, a descriptive value and a dynamic attribute of the relation between target nodes; and
based on an outputted result of the result outputting network, updating a parameter of the individual operator network.