# US 2021/0279422 A1 · Filtering spurious knowledge graph relationships between labeled entities > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279422 A1 - Document ID: US20210279422A1 - Status: Published application - Kind: A1 - Application number: 17/323,584 - Filed: 2021-05-18 - Published: 2021-09-09 - Earliest claimed: 2019-10-14 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Nandana Mihindukulasooriya; Robert G. Farrell; Nicolas Rodolfo Fauceglia; Alfio Massimiliano Gliozzo - Web page: https://patentgrep.com/patent/US20210279422A1 ## CPC - G06F 40/30: Semantic analysis - G06F 16/335: Filtering based on additional data, e.g. user or group profiles (filtering in web context G06F16/9535, G06F16/9536) - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F 40/211: Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars - G06F 40/247: Thesauruses; Synonyms - G06F 40/268: Morphological analysis - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 40/295: Named entity recognition - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and techniques that facilitate spurious relationship filtration from external knowledge graphs based on distributional semantics of an input corpus are provided. In one or more embodiments, a context component can generate a context-based word embedding of one or more first terms in a document collection. The embedding can yield vector representations of the one or more first terms. The one or more first terms can correspond to knowledge terms in one or more first nodes of a knowledge graph. In one or more embodiments, a filtering component can filter out a relationship between the one or more first nodes and a second node of the knowledge graph based on a similarity value being less than a threshold. The similarity value can be a function of the vector representations of the one or more first terms. In various embodiments, cosine similarity can be used to compute the similarity value. ## Claims ### Claim 1 A system, comprising: a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory, wherein the computer-executable components comprise: a context component that: generates a context-based embedding of one or more first terms in a document collection; and determines that at least two of the one or more first terms that have vector representations that have a similarity value that meets a defined criterion have a hypernymy relation between them, and determines that at least two of the one or more first terms that have vector representations that fail to have a similarity value that meets the defined criterion do not have a hypernymy relation between them. ### Claim 2 (depends on 1) The system of claim 1, wherein: the similarity value is based on a cosine similarity between the vector representations of the one or more first terms and a vector representation of a second term in the document collection corresponding to the second node. ### Claim 3 (depends on 1) The system of claim 1, wherein: the similarity value is based on average pairwise cosine similarities between the vector representations of the one or more first terms. ### Claim 4 (depends on 1) The system of claim 1, wherein: the similarity value is based on cosine similarities between the vector representations of the one or more first terms and a vector representation of a prototypical term in the one or more first terms. ### Claim 5 (depends on 1) The system of claim 1, wherein the knowledge terms are part of one or more labels, descriptions, definitions, or other text associated with the one or more first nodes. ### Claim 6 (depends on 1) The system of claim 1, wherein the relation is one from the group consisting of a hypernym-hyponym relation, a synonymy relation, an antonymy relation, an entailment relation, and a partonomy relation. ### Claim 7 (depends on 1) The system of claim 1, wherein the one or more first terms correspond to the knowledge terms lexically, orthographically, morphologically, syntactically, or semantically. ### Claim 8 (depends on 1) The system of claim 1, wherein the context component generates the embedding of the one or more first terms via a neural network that employs a Continuous Bag of Words or Skip Gram methodology. ### Claim 9 A computer-implemented method, comprising: generating, by a device operatively coupled to a processor, a context-based embedding of one or more first terms in a document collection; and determining, by the device, that at least two of the one or more first terms that have vector representations that have a similarity value that meets a defined criterion have a hypernymy relation between them, and determine that at least two of the one or more first terms that have vector representations that fail to have a similarity value that meets the defined criterion do not have a hypernymy relation between them, wherein the vector representations are compared to determine whether the hypernymy relation from the knowledge graph for the at least two of the one or more first terms is spurious. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein: the similarity value is based on a cosine similarity between the vector representations of the one or more first terms and a vector representation of a second term in the document collection corresponding to the second node. ### Claim 11 (depends on 9) The computer-implemented method of claim 9, wherein: the similarity value is based on average pairwise cosine similarities between the vector representations of the one or more first terms. ### Claim 12 (depends on 9) The computer-implemented method of claim 9, wherein: the similarity value is based on cosine similarities between the vector representations of the one or more first terms and a vector representation of a prototypical term in the one or more first terms. ### Claim 13 (depends on 9) The computer-implemented method of claim 9, wherein the knowledge terms are part of one or more labels, descriptions, definitions, or other text associated with the one or more first nodes. ### Claim 14 (depends on 9) The computer-implemented method of claim 9, wherein the relationship is one from the group consisting of a hypernym-hyponym relation, a synonymy relation, an antonymy relation, an entailment relation, and a partonomy relation. ### Claim 15 (depends on 9) The computer-implemented method of claim 9, wherein the one or more first terms correspond to the knowledge terms lexically, orthographically, morphologically, syntactically, or semantically. ### Claim 16 (depends on 9) The computer-implemented method of claim 9, wherein the generating the embedding of the one or more first terms is performed with a neural network that employs a Continuous Bag of Words or Skip Gram methodology. ### Claim 17 A computer program product for facilitating spurious relationship filtration, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing component to cause the processing component to: generate a context-based embedding of one or more first terms in a document collection; and determine that at least two of the one or more first terms that have vector representations that have a similarity value that meets a defined criterion have a supplier relation between them, and determine that at least two of the one or more first terms that have vector representations that fail to have a similarity value that meets the defined criterion do not have a supplier relation between them, wherein the vector representations are compared to determine whether the supplier relation from the knowledge graph for the at least two of the one or more first terms is spurious. ### Claim 18 (depends on 17) The computer program product of claim 17, wherein: the similarity value is based on a cosine similarity between the vector representations of the one or more first terms and a vector representation of a second term in the document collection corresponding to the second node. ### Claim 19 (depends on 17) The computer program product of claim 17, wherein: the similarity value is based on average pairwise cosine similarities between the vector representations of the one or more first terms. ### Claim 20 (depends on 17) The computer program product of claim 17, wherein: the similarity value is based on cosine similarities between the vector representations of the one or more first terms and a vector representation of a prototypical term in the one or more first terms.