# US 11,907,662 B2 · System and method for domain-independent terminology linking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,907,662 B2 - Document ID: US11907662B2 - Status: Granted - Kind: B2 - Application number: 16/958,888 - Filed: 2018-12-27 - Published: 2020-10-29 - Granted: 2024-02-20 - Earliest claimed: 2017-12-29 - Assignees: Robert Bosch GmbH - Applicants: Robert Bosch GmbH - Inventors: Haibo Ding; Yifan He; Lin Zhao; Kui Xu; Zhe Feng - Web page: https://patentgrep.com/patent/US11907662B2 ## CPC - G06F 40/30: Semantic analysis - G06F 16/36: Creation of semantic tools, e.g. ontology or thesauri - G06F 40/169: Annotation, e.g. comment data or footnotes - G06F 40/247: Thesauruses; Synonyms - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/04: Inference or reasoning models - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract An automatic terminology linking system includes a candidate generator configured to identify candidate nodes for each terminology that is to be linked to a node of the knowledge base. A pseudo-candidate generator is configured to identify pseudo-candidate nodes for candidate-less terminologies. A candidate scorer is configured to respectively score the candidate nodes and the pseudo-candidate nodes by collective inference using occurrence statistics and co-occurrence statistics for these nodes. The pseudo-candidate generator is configured to identify knowledge base nodes that are semantically-related to candidate-less terminology as the pseudo-candidate nodes for the candidate-less terminology. ## Claims ### Claim 1 An automatic terminology linking system for automatically linking terminology to nodes of a knowledge base, the system comprising: a processor configured to: identify candidate nodes for each terminology that is to be linked to a node of the knowledge base using occurrence statistics, wherein each terminology determined to have no candidates is identified as candidate-less terminology; identify pseudo-candidate nodes for the candidate-less terminology that is to be linked to a node of the knowledge base, the pseudo-candidate nodes being semantically-related to candidate-less terminology; respectively score the candidate nodes and the pseudo-candidate nodes by collective inference using occurrence statistics and co-occurrence statistics for nodes of the knowledge base corresponding to the candidates and pseudo-candidates, link each terminology to the node of the knowledge base corresponding to the highest-scored candidate node for the terminology; and link each candidate-less terminology to the node of the knowledge base corresponding to the highest-scored pseudo-candidate for the candidate-less terminology, wherein the occurrence statistics indicate probability that a terminology is linked to a respective node of the knowledge base, and wherein the co-occurrence statistics indicate (i) a probability that two terminologies appear in a same document or (ii) a probability that two nodes of the knowledge based are linked to by a same document. ### Claim 2 (depends on 1) The system of claim 1, the processor being further configured to: determine the occurrence statistics and the co-occurrence statistics for the nodes of the knowledge base using a collection of documents. ### Claim 3 (depends on 1) The system of claim 1, the processor being further configured to: receive a document and a list of terminologies in the document as input. ### Claim 4 (depends on 3) The system of claim 3, the processor being further configured to: generate technical documents with terminologies linked to associated nodes of the knowledge base. ### Claim 5 (depends on 1) The system of claim 1, the processor being further configured to: identify knowledge base nodes that are near-synonyms to the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 6 (depends on 1) The system of claim 1, the processor being further configured to: identify knowledge base nodes that share words with the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 7 (depends on 1) The system of claim 1, the processor being further configured to: identify knowledge base nodes that are distributionally similar to the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 8 (depends on 1) The system of claim 1, the processor being further configured to: determine a distributional similarity between the candidate-less terminology and the nodes of the knowledge base using vector representations of the knowledge base nodes and the unlinked terminology. ### Claim 9 A method for automatically linking terminology to nodes of a knowledge base, the method comprising: identifying candidate nodes for each terminology that is to be linked to a node of the knowledge base using occurrence statistics, wherein each terminology determined to have no candidates is identified as candidate-less terminology; identifying pseudo-candidate nodes for the candidate-less terminology that is to be linked to a node of the knowledge base, the pseudo-candidate nodes being semantically-related to candidate-less terminology; scoring the candidate nodes and the pseudo-candidate nodes by collective inference using occurrence statistics and co-occurrence statistics for nodes of the knowledge base; linking each terminology to the highest-scored candidate for that terminology; and linking each candidate-less terminology with the highest-scored pseudo-candidate for that candidate-less terminology, wherein the occurrence statistics indicate probability that a terminology is linked to a respective node of the knowledge base, and wherein the co-occurrence statistics indicate (i) a probability that two terminologies appear in a same document or (ii) a probability that two nodes of the knowledge based are linked to by a same document. ### Claim 10 (depends on 9) The method of claim 9, further comprising: determining the occurrence statistics and the co-occurrent statistics for the nodes of the knowledge base using a collection of documents. ### Claim 11 (depends on 9) The method of claim 9, further comprising: receiving a document and a list of terminologies in the document as input to identifying the candidate nodes. ### Claim 12 (depends on 11) The method of claim 11, further comprising: generating a technical document with terminologies linked to associated nodes of the knowledge base. ### Claim 13 (depends on 9) The method of claim 9, the identifying pseudo-candidate nodes further comprising: identifying knowledge base nodes that are near-synonyms to the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 14 (depends on 9) The method of claim 9, the identifying pseudo-candidate nodes further comprising: identifying knowledge base nodes that share words with the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 15 (depends on 9) The method of claim 9, the identifying pseudo-candidate nodes further comprising: identifying knowledge base nodes that are distributionally similar to the candidate-less terminology as pseudo-candidates for the candidate-less terminology. ### Claim 16 (depends on 9) The method of claim 9, further comprising: determining a distributional similarity between the candidate-less terminology and the nodes of the knowledge base using vector representations of the knowledge base nodes and the unlinked terminology.