17. A computing system for extending a knowledge graph personalized for a target user, comprising: one or more computer processors; computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations comprising: aggregating usage history for a first user from one or more sources, wherein the usage history for the first user comprises one or more past user utterances; extracting entity and user action information from the usage history; mapping the extracted entity and user action information onto a first knowledge graph for the first user, thereby creating a first personalized knowledge graph, the first personalized knowledge graph comprising a second entity and a triple indicating a relationship between the extracted entity and user action information and the second entity, the mapping comprising at least one of incrementing a count of the one or more of the entity or calculating a weight of the one or more of the entity; determining a second user similar to the first user; extending the first personalized knowledge graph with information from a second personalized knowledge graph for the second user; utilizing the extended first personalized knowledge graph to personalize a language model for the second user thereby forming a second-user personalized language model; and utilizing the second-user personalized language model for computer-performed speech recognition or spoken language understanding for the second user.
17. A computing system for extending a knowledge graph personalized for a target user, comprising: one or more computer processors; computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations comprising: aggregating usage history for a first user from one or more sources, wherein the usage history for the first user comprises one or more past user utterances; extracting entity and user action information from the usage history; mapping the extracted entity and user action information onto a first knowledge graph for the first user, thereby creating a first personalized knowledge graph, the first personalized knowledge graph comprising a second entity and a triple indicating a relationship between the extracted entity and user action information and the second entity, the mapping comprising at least one of incrementing a count of the one or more of the entity or calculating a weight of the one or more of the entity; determining a second user similar to the first user; extending the first personalized knowledge graph with information from a second personalized knowledge graph for the second user; utilizing the extended first personalized knowledge graph to personalize a language model for the second user thereby forming a second-user personalized language model; and utilizing the second-user personalized language model for computer-performed speech recognition or spoken language understanding for the second user. 20. The computing system of claim 17 , wherein the second user is determined to be similar to the first user based on clustering or a relationship with the first user in a social network.