Invention Grant
- Patent Title: Causal knowledge identification and extraction
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Application No.: US17354171Application Date: 2021-06-22
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Publication No.: US11922129B2Publication Date: 2024-03-05
- Inventor: Manik Bhandari , Oktie Hassanzadeh , Mark David Feblowitz , Kavitha Srinivas , Shirin Sohrabi Araghi
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Mark Bergner
- Main IPC: G06F7/00
- IPC: G06F7/00 ; G06F40/40 ; G06N5/04 ; G06N20/00

Abstract:
A computer-implemented method is provided that includes accessing candidate text and a candidate pair including first and second phrases, substituting the first and second phrases into cause-effect patterns to generate variant sentences. An artificial intelligence model is leveraged to determine respective probabilities that the variant sentences are inferred from the candidate text, calculate a statistical measure of the respective probabilities, and assess the calculated statistical measure to ascertain whether the first and second phrases possess a causal relationship or non-causal relationship to one another. A knowledge base including one or more pairs of cause-effect phrase pairs is populated with the first and second phrases possessing the causal relationship. A computer system and a computer program product are also provided.
Public/Granted literature
- US20220405487A1 Causal Knowledge Identification and Extraction Public/Granted day:2022-12-22
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