公开(公告)号：GB2592335A

公开(公告)日：2021-08-25

申请号：GB202108987

申请日：2019-11-22

Applicant: IBM

Inventor： TIM SCHEIDELER ,  ERIK RUEGER ,  STEFAN RAVIZZA ,  FREDERIK FLOETHER

IPC: G06F16/35 ,  G06F16/901 ,  G06F16/906

Abstract: A method for partitioning a knowledge graph is provided. The method analyzes past searches and determines an access frequency of a plurality of edges. The method marks, as intermediate cluster cores, edges having the highest access frequencies, sorts the marked 5intermediate cluster cores according to their access frequencies, and selects a first cluster core having the highest access frequency. The method assigns first edges in a first radiusaround the first cluster core to build the first cluster. The method selects a second cluster core having the highest access frequency apart from edges of the first cluster, and assignssecond edges in a second radius around second cluster core to build the second cluster. The 0method partitions the knowledge graph into a first sub-knowledge-graph comprising the first cluster and a second sub-knowledge-graph comprising the second cluster.

公开(公告)号：GB2597406A

公开(公告)日：2022-01-26

申请号：GB202115858

申请日：2020-03-18

Applicant: IBM

Inventor： GEORGIOS CHALOULOS ,  FREDERIK FLOETHER ,  FLORIAN GRAF ,  PATRICK LUSTENBERGER ,  STEFAN RAVIZZA ,  ERIC SLOTTKE

IPC: G06N20/00

Abstract: A computer-implemented method for improving fairness in a supervised machine-learning model may be provided. The method comprises linking the supervised machine-learning model to a reinforcement learning meta model, selecting a list of hyper-parameters and parameters of the supervised machine-learning model, and controlling at least one aspect of the supervised machine-learning model by adjusting hyper-parameters values and parameter values of the list of hyper-parameters and parameters of the supervised machine-learning model by a reinforcement learning engine relating to the reinforcement learning meta model by calculating a reward function based on multiple conflicting objective functions. The method further comprises repeating iteratively the steps of selecting and controlling for improving a fairness value of the supervised machine-learning model.