# US 2023/0267338 A1 · Keyword based open information extraction for fact-relevant knowledge graph creation and link prediction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0267338 A1 - Document ID: US20230267338A1 - Status: Published application - Kind: A1 - Application number: 17/734,129 - Filed: 2022-05-02 - Published: 2023-08-24 - Earliest claimed: 2022-02-18 - Assignees: NEC Laboratories Europe GmbH - Applicants: NEC Laboratories Europe GmbH - Inventors: Bhushan Kotnis; Kiril Gashteovski; Carolin Lawrence - Web page: https://patentgrep.com/patent/US20230267338A1 ## CPC - G06N 5/02: Knowledge representation; Symbolic representation - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06F 16/3326: {using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages} - G06F 16/3338: {Query expansion} - G06F 16/338: Presentation of query results - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F 18/213: Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods - G06F 18/24: Classification techniques - G06F 18/29: {Graphical models, e.g. Bayesian networks} - G06F 18/40: Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method for automated decision making in an artificial intelligence task by fact-relevant open information extraction and knowledge graph generation includes obtaining a keyword query for performing the fact-relevant open information extraction and expanding the keyword query using keyword alias and query generation. The fact-relevant open information extraction is performed to extract triples from a text which contains the keyword or the keyword alias. The knowledge graph is generated using the extracted triples and an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases. Supervised or unsupervised classification is performed using the generated knowledge graph to make the automated decision in the artificial intelligence task. ## Claims ### Claim 1 A method for automated decision making in an artificial intelligence task by fact-relevant open information extraction and knowledge graph generation, the method comprising: obtaining a keyword query for performing the fact-relevant open information extraction; expanding the keyword query using keyword alias and query generation; performing the fact-relevant open information extraction to extract triples from a text which contains the keyword or the keyword alias; generating the knowledge graph using the extracted triples using an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases; and performing supervised or unsupervised classification and the generated knowledge graph to make the automated decision in the artificial intelligence task. ### Claim 2 (depends on 1) The method according to claim 1, further comprising obtaining a context query, expanding the context query using context alias and query generation, and performing the fact-relevant open information extraction to extract the triples from the text which contain the context or the context alias, and the keyword or the keyword alias. ### Claim 3 (depends on 2) The method according to claim 2, further comprising displaying the aliases and queries to a user, and updating the aliases and/or the queries based on a user input. ### Claim 4 (depends on 1) The method according to claim 1, further comprising displaying the knowledge graph to a user, and pruning the knowledge graph based on a user input. ### Claim 5 (depends on 1) The method according to claim 1, further comprising pruning the generated knowledge graph by at least one of temporal, location or triple pruning. ### Claim 6 (depends on 1) The method according to claim 1, wherein the keyword query is obtained from a recommendation system. ### Claim 7 (depends on 1) The method according to claim 1, wherein the supervised classification is performed using a Gumbel softmax. ### Claim 8 (depends on 1) The method according to claim 1, wherein the unsupervised classification is performed using a relational page rank algorithm. ### Claim 9 (depends on 1) The method according to claim 1, wherein the OpenKG extractor has been trained using different keywords and context from a different source text, wherein each of the keywords and the respective context are combined at nodes in the knowledge graph. ### Claim 10 (depends on 1) The method according to claim 1, wherein the automated decision includes one of adapting parameters of a device or digital display, or manufacturing or providing instructions for manufacturing of a product. ### Claim 11 A system for automated decision making in an artificial intelligence task by fact-relevant open information extraction and knowledge graph generation, the system comprising one or more hardware processors configured, alone or in combination, to provide for execution of the following steps: obtaining a keyword query for performing the fact-relevant open information extraction; expanding the keyword query using keyword alias and query generation; performing the fact-relevant open information extraction to extract triples from a text which contains the keyword or the keyword alias; generating the knowledge graph using the extracted triples and an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases; and performing supervised or unsupervised classification using the generated knowledge graph to make the automated decision in the artificial intelligence task. ### Claim 12 (depends on 11) The system according to claim 11, being further configured to obtain a context query, expand the context query using context alias and query generation, and perform the fact-relevant open information extraction to extract the triples from the text which contain the context or the context alias, and the keyword or the keyword alias. ### Claim 13 (depends on 11) The system according to claim 11, wherein the OpenKG extractor has been trained using different keywords and context from a different source text, wherein each of the keywords and the respective context are combined at nodes in the knowledge graph. ### Claim 14 (depends on 11) The system according to claim 11, wherein the automated decision includes one of adapting parameters of a device or digital display, or manufacturing or providing instructions for manufacturing of a product. ### Claim 15 A tangible, non-transitory computer-readable medium having instructions thereon, which, upon being executed by one or more processors provide for execution of the following steps: obtaining a keyword query for performing the fact-relevant open information extraction; expanding the keyword query using keyword alias and query generation; performing the fact-relevant open information extraction to extract triples from a text which contains the keyword or the keyword alias; generating the knowledge graph using the extracted triples and an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases; and performing supervised or unsupervised classification using the generated knowledge graph to make the automated decision in the artificial intelligence task.