Self-learning system and method for automatically performing machine learning

Patent metadata

Field	Value
Publication number	US-2018189679-A1
Application number	US-201815859937-A
Country	US
Kind code	A1
Filing date	Jan 2, 2018
Priority date	Jan 3, 2017
Publication date	Jul 5, 2018
Grant date	—

Abstract

Provided are a self-learning system and method for automatically performing machine learning (ML). The self-learning system includes a memory configured to store an ML knowledge database (DB) in which ML knowledge is stored and a program for automatically performing ML based on request information of a user, and a processor configured to execute the program stored in the memory. Here, when executing the program, the processor creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge stored in the ML knowledge DB and generates an execution code for performing the created or recommended workflow.

First claim

What is claimed is:

1 . A self-learning system for automatically performing machine learning (ML), the system comprising:
a memory configured to store an ML knowledge database (DB) in which ML knowledge is stored and a program for automatically performing ML based on request information of a user; and a processor configured to execute the program stored in the memory, wherein when executing the program, the processor creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge stored in the ML knowledge DB and generates an execution code for performing the created or recommended workflow.

2 . The self-learning system of claim 1 , wherein the ML knowledge DB includes at least one of user knowledge obtained by transforming scope of modification in workflow based on user type into knowledge, domain knowledge obtained by transforming scope of modification in workflow based on features of analysis-target domains into knowledge, guide knowledge in which information structures for generating workflow steps are defined, and workflow knowledge obtained by transforming applicable workflows based on user type and domain type into knowledge.

3 . The self-learning system of claim 2 , wherein the processor creates at least one workflow corresponding to the request information of the user based on at least one of the user knowledge, the domain knowledge, the guide knowledge, and the workflow knowledge.

4 . The self-learning system of claim 2 , wherein the user knowledge is structured to include user type information, user operating environment information, and setting depth information for defining user-setting ranges of workflow or automatic-setting workflow ranges based on users and user type.

5 . The self-learning system of claim 2 , wherein the domain knowledge is structured to include domain type information and problem type information indicating a type of a problem to be solved by the domain type.

6 . The self-learning system of claim 2 , wherein the guide knowledge is structured to include at least one of location information knowledge, data condition knowledge, model restriction knowledge, execution restriction knowledge, and use-experience knowledge.

7 . The self-learning system of claim 6 , wherein the location information knowledge includes at least one of a data storage location required to perform the workflow and an access route of a software package,
the data condition knowledge includes at least one of a specific workflow for defining the workflow, a specific model element, and information on input and output data conditions of a specific class, the model restriction knowledge includes knowledge for restricting executable workflows or executable ML models, the execution restriction knowledge includes at least one of domain restriction knowledge, data restriction knowledge, memory restriction knowledge, and hardware restriction knowledge about a specific ML model, and the use-experience knowledge includes at least one of a prediction type, frequencies of use of ML models, a label, and information about whether a label is necessary.

8 . The self-learning system of claim 6 , wherein the guide knowledge has an if-then-else structure with regard to the model restriction knowledge and the execution restriction knowledge, and
the processor automatically obtains the restriction knowledge through information on a result of performing the workflow.

9 . The self-learning system of claim 2 , wherein the workflow knowledge comprises a plurality of nodes for defining individual unit functions constituting the workflow, attribute information of the nodes, and inter-node connection information.

10 . The self-learning system of claim 9 , wherein the plurality of nodes include at least two of a task starting node, a data processing node, a conditional branch node, and a task ending node.

11 . The self-learning system of claim 2 , wherein the ML knowledge DB further includes logical knowledge obtained by transforming a function available in the workflow into knowledge, and
the processor concretizes the created workflow to a logical knowledge level based on the logical knowledge.

12 . The self-learning system of claim 11 , wherein the logical knowledge is mapped to 0 or more entities of physical knowledge.

13 . The self-learning system of claim 2 , wherein the ML knowledge DB further includes physical knowledge for defining model elements at a software library level available in the workflow, and
the processor generates the execution code of the workflow based on the physical knowledge.

14 . The self-learning system of claim 13 , wherein the processor collects the request information of the user including an analysis-target domain type and a user type requested to be analyzed, creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge DB, and generates the execution code based on the physical knowledge included in the ML knowledge DB.

15 . The self-learning system of claim 14 , wherein before generating the execution code, the processor concretizes the recommended at least one workflow to a logical knowledge level based on logical knowledge included in the ML knowledge DB, and converts the concretized workflow to an execution code level.

16 . The self-learning system of claim 14 , wherein the processor executes the at least one workflow based on the generated execution code and updates the ML knowledge DB by feeding back a result of the at least one workflow.

17 . The self-learning system of claim 16 , wherein when no workflow corresponds to the request information of the user in the ML knowledge DB, the processor creates a plurality of workflows applicable to the analysis-target domain type included in the request information of the user, analyzes performance of the created workflows by comparing results of performing the workflows, and selects and provides the at least one workflow to be recommended among the plurality of workflows.

18 . A self-learning method for automatically performing machine learning (ML), the method comprising:
receiving request information of a user including a user type requested to be analyzed and an analysis-target domain type; creating or recommending at least one workflow corresponding to the request information of the user based on ML knowledge stored in an ML knowledge database (DB); and generating an execution code for performing the created or recommended workflow.

19 . The self-learning method of claim 18 , wherein the ML knowledge DB includes at least one of user knowledge obtained by transforming workflow ranges based on user type into knowledge, domain knowledge obtained by transforming workflow ranges based on features of analysis-target domains into knowledge, guide knowledge in which information structures for generating workflow steps of a workflow are defined, workflow knowledge obtained by transforming applicable workflows based on user type and domain type into knowledge, logical knowledge obtained by transforming functions available in the workflow into knowledge, and physical knowledge for defining model elements at a software library level available in the workflow.

20 . The self-learning method of claim 19 , wherein the creating or recommending of the at least one workflow comprises:
creating at least one workflow corresponding to the request information of the user based on at least one of the user knowledge, the dom

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Kang Hyun Joong
• Kim Hyun Jae
• Lee Ho Sung
• Kwon Soon Hyun
• Kim Kwi Hoon
• Kim Young Min
• Kim Eun Joo
• Park Hong Kyu
• Bae Ji Hoon
• Oh Se Won
• Yu Jae Hak
• Lee Yeon Hee
• Kim Nae Soo
• Kim Sun Jin
• Cho Seong Ik
• Pyo Cheol Sig

Classifications

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N99/005 Primary

  Physics · mapped topic

• G06N20/00 Primary

  Machine learning · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title