# US 2020/0401662 A1 · Text classification with semantic graph for detecting health care policy changes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401662 A1 - Document ID: US20200401662A1 - Status: Published application - Kind: A1 - Application number: 16/448,658 - Filed: 2019-06-21 - Published: 2020-12-24 - Earliest claimed: 2019-06-21 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Feng-wei Chen; John Segrave-Daly; Conor Patrick Cullen - Web page: https://patentgrep.com/patent/US20200401662A1 ## CPC - G06F 40/30: Semantic analysis - G06F 40/237: Lexical tools - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract According to one embodiment, a method, computer system, and computer program product for detecting and communicating semantic changes in revisions of two or more text documents is provided. The present invention may include converting two or more text documents into semantic graphs; comparing the semantic graphs, to identify a the semantic differences between the text documents, wherein the comparing entails applying both a coarse-grained differencing method and a fine-grained differencing method to identify of the changes between equivalent sections of the text documents; and transmitting, based on user preferences, a subset of the semantic differences to one or more user devices. ## Claims ### Claim 1 A processor-implemented method for semantic text analysis, the method comprising: converting two or more text documents into a plurality of semantic graphs; comparing two or more of the plurality of semantic graphs, to identify a plurality of semantic differences between the two or more text documents; transmitting, based on a user selection, a subset of the semantic differences to one or more user devices. ### Claim 2 (depends on 1) The method of claim 1, wherein comparing two or more of the plurality of semantic graphs further comprises: classifying a plurality of types of an ontology into dimensions, wherein the ontology is related to the field of the one or more text documents; identifying, using coarse-grained differencing, two or more equivalent sections of two or more documents; identifying two or more candidate sub-graphs corresponding to the two or more equivalent sections that match with the user selection; and comparing, using fine-grained differencing, two or more candidate sub-graphs that match with the user selection to enumerate the one or more semantic differences between the two or more candidate sub-graphs. ### Claim 3 (depends on 2) The method of claim 2, wherein coarse-grained differencing comprises text comparison. ### Claim 4 (depends on 2) The method of claim 2, wherein fine-grained differencing comprises semantic graph analysis. ### Claim 5 (depends on 2) The method of claim 2, wherein dimensions are one or more semantic categories containing one or more semantically linked domains, ranges, or properties of the ontology. ### Claim 6 (depends on 1) The method of claim 1, wherein the subset of changes are anchored to the plurality of text to which they pertain. ### Claim 7 (depends on 1) The method of claim 1, further comprising: generating a micro summary semantic graph based on the subset of semantic differences. ### Claim 8 A computer system for semantic text analysis, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: converting two or more text documents into a plurality of semantic graphs; comparing two or more of the plurality of semantic graphs, to identify a plurality of semantic differences between the two or more text documents; transmitting, based on a user selection, a subset of the semantic differences to one or more user devices. ### Claim 9 (depends on 8) The computer system of claim 8, wherein comparing two or more of the plurality of semantic graphs further comprises: classifying a plurality of types of an ontology into dimensions, wherein the ontology is related to the field of the one or more text documents; identifying, using coarse-grained differencing, two or more equivalent sections of two or more documents; identifying two or more candidate sub-graphs corresponding to the two or more equivalent sections that match with the user selection; and comparing, using fine-grained differencing, two or more candidate sub-graphs that match with the user selection to enumerate the one or more semantic differences between the two or more candidate sub-graphs. ### Claim 10 (depends on 9) The computer system of claim 9, wherein coarse-grained differencing comprises text comparison. ### Claim 11 (depends on 9) The computer system of claim 9, wherein fine-grained differencing comprises semantic graph analysis. ### Claim 12 (depends on 9) The computer system of claim 9, wherein dimensions are one or more semantic categories containing one or more semantically linked domains, ranges, or properties of the ontology. ### Claim 13 (depends on 8) The computer system of claim 8, wherein the subset of changes are anchored to the plurality of text to which they pertain. ### Claim 14 (depends on 8) The computer system of claim 8, further comprising: generating a micro summary semantic graph based on the subset of semantic differences. ### Claim 15 A computer program product for semantic text analysis, the computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor to cause the processor to perform a method comprising: converting two or more text documents into a plurality of semantic graphs; comparing two or more of the plurality of semantic graphs, to identify a plurality of semantic differences between the two or more text documents; transmitting, based on a user selection, a subset of the semantic differences to one or more user devices. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein comparing two or more of the plurality of semantic graphs further comprises: classifying a plurality of types of an ontology into dimensions, wherein the ontology is related to the field of the one or more text documents; identifying, using coarse-grained differencing, two or more equivalent sections of two or more documents; identifying two or more candidate sub-graphs corresponding to the two or more equivalent sections that match with the user selection; and comparing, using fine-grained differencing, two or more candidate sub-graphs that match with the user selection to enumerate the one or more semantic differences between the two or more candidate sub-graphs. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein coarse-grained differencing comprises text comparison. ### Claim 18 (depends on 16) The computer program product of claim 16, wherein fine-grained differencing comprises semantic graph analysis. ### Claim 19 (depends on 16) The computer program product of claim 16, wherein dimensions are one or more semantic categories containing one or more semantically linked domains, ranges, or properties of the ontology. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the subset of changes are anchored to the plurality of text to which they pertain.