Semantic entity relation detection classifier training implementations are presented that are generally used to train a semantic entity relation detection classifier to identify relations expressed in a natural language query. In one general implementation, queries are found in a search query click log that exhibit relations and entity types found in a semantic knowledge graph. Explicit relations are inferred from the found queries and an explicit relations data set is generated that includes queries associated with the inferred explicit relations. In addition, implicit relations are inferred from the found queries and an implicit relations data set is generated that includes queries associated with the inferred implicit relations. A semantic entity relation detection classifier is then trained using the explicit and implicit data sets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A semantic entity relation detection classifier training system, comprising: one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices; and a computer program having program modules executable by the one or more computing devices, the one or more computing devices being directed by the program modules of the computer program to, receive a query click log and a knowledge graph, find queries included in the query click log that are associated with entities found in the knowledge graph, said entities being associated with a knowledge graph domain of interest, infer explicit relations from the found queries and generate an explicit relations data set comprising queries associated with the inferred explicit relations, infer implicit relations from the found queries and generate an implicit relations data set comprising queries associated with the inferred implicit relations, and train a semantic entity relation detection classifier using the explicit and implicit data sets to find an explicit or implicit relation, or both, in a query. 2. The system of claim 1, wherein the program module for finding queries included in the query click log that are associated with entities found in the knowledge graph, comprises sub-modules for: identifying one or more central entity types in the knowledge graph which correspond to a domain of interest; for each identified central entity type, finding central type entities in the knowledge graph that correspond to the central entity type under consideration, establishing a central entity type property list for each of the found central type entities that comprises the found central type entity and other entities in the knowledge graph having a prescribed degree of relation to the central type entity under consideration, as well as the type of relation existing between the found central type entity and each of the other entities in the knowledge graph having a prescribed degree of relation to the central type entity under consideration, combining the central entity type property list established for the identified central entity types to produce a combined entity property list, and finding queries associated with entities listed in the combined entity property list in the query click log. 3. The system of claim 2, wherein the sub-module for finding queries associated with entities listed in the combined entity property list in the query click log, comprises sub-modules for: creating a seed query from an entity in the combined entity property list; finding query click log queries that include the seed query; identifying uniform resource locators (URLs) from the query click log that are associated with at least one of the found queries; and finding other queries in the query click log that are associated with at least one of the identified URLs. 4. The system of claim 3, wherein the sub-module for finding queries associated with entities listed in the combined entity property list in the query click log, further comprises a sub-module for eliminating the query click log queries found to include the seed query from consideration prior to identifying URLs that do not meet a prescribed length criteria, or quantity criteria, or both. 5. The system of claim 2, wherein the sub-module for finding queries associated with entities listed in the combined entity property list in the query click log, comprises sub-modules for: identifying one or more relations of an entity in the combined entity property list each of which points to at least one URL in the knowledge graph; generating a list of the pointed to URLs; and finding queries in the query click log that are associated with at least one of the listed URLs. 6. The system of claim 2, further comprising a sub-module for, after queries associated with entities listed in the combined entity property list in the query click log are found, eliminating from consideration those found queries that are non-natural spoken language queries. 7. The system of claim 1, wherein an entity in the knowledge graph has said prescribed degree of relation to a central type entity whenever the entity is associated with an incoming relation from the central type entity, or is reachable in the knowledge graph from the central type entity within a prescribed number of relations. 8. The system of claim 1, wherein the program module for inferring explicit relations from the found queries and generating an explicit relations data set comprising queries associated with the inferred explicit relations, comprises sub-modules for: scanning the found queries to find those queries exhibiting an inferred explicit relation between entities wherein an inferred explicit relation between entities is defined as the presence of an entity and a closely related entity in the same query, and wherein an entity is closely related to another entity whenever the entity is connected to the another entity in the knowledge graph by no more than a prescribed number of intermediate entities; determining the types of relation exhibited by a pair of entities in each query exhibiting an inferred explicit relation; and generating an explicit relations data set comprising the text of queries associated with the inferred explicit relations as well as the type of relation assigned to each of the entities in the pair. 9. The system of claim 8, wherein said prescribed number of intermediate entities is one, such that entities that were directly connected to each other are considered closely related, as well as entities that are connected to another entity by no more than one intermediate entity. 10. The system of claim 8, wherein the sub-module for scanning the found queries to find those queries exhibiting an inferred explicit relation between entities, comprises sub-modules for: determining if an entity associated with a found query is connected in the knowledge graph to another entity by a directed connector or path of connectors originating at the entity associated with a found query by no more than the prescribed number of intermediate entities; whenever the entity associated with the found query is connected in the knowledge graph to another entity by a directed connector or path of connectors originating at the entity associated with a found query by no more than the prescribed number of intermediate entities, determining if said other entity is also contained in the found query; and whenever said other entity is also contained in the found query, designating the found query as exhibiting an inferred explicit relation between the entities. 11. The system of claim 10, wherein the sub-module for scanning the found queries to find those queries exhibiting an inferred explicit relation between entities, further comprises sub-modules for: for a query designated as exhibiting an inferred explicit relation between a pair of entities contained therein, identifying the relation label assigned to each connector connecting the pair of entities in the knowledge graph; determining the relation of said other entity of the entity pair based on the identify relation label or labels and assigning the determined relation to said other entity of the entity pair; and assigning the relation of the entity associated with a found query, if known, to that entity of the entity pair. 12. The system of claim 8, wherein the sub-module for scanning the found queries to find those queries exhibiting an inferred explicit relation between entities, comprises sub-modules for: identifying an entity pair in the knowledge graph having a first entity of the pair that is connected to another entity of the pair by a directed connector or path of connectors originating at the first entity by no more than the prescribed number of intermediate entities, and whose connector or connectors connecting the pair of entities have relation label or labels that correspond to a semantic entity relation type associated with a domain of interest; determining if a found query contains the identified entity pair; and whenever the found query contains the identified entity pair, designating the found query as exhibiting an inferred explicit relation between the entities, assigning the semantic entity relation type associated with the domain of interest to said other entity of the entity pair, and assigning the relation of the first entity of the pair, if known, to that entity. 13. A semantic entity relation detection classifier training system, comprising: one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices; and a computer program having program modules executable by the one or more computing devices, the one or more computing devices being directed by the program modules of the computer program to, receive a query click log and a knowledge graph, find queries included in the query click log that are associated with entities found in the knowledge graph, said entities being associated with a knowledge graph domain of interest, infer implicit relations from the found queries and generate an implicit relations data set comprising queries associated with the inferred implicit relations, and train a semantic entity relation detection classifier using at least the implicit data set to find a relation in a query. 14. The system of claim 13, wherein the program module for finding queries included in the query click log that are associated with entities found in the knowledge graph, comprises sub-modules for: identifying one or more central entity types in the knowledge graph which correspond to a domain of interest; for each identified central entity type, finding central type entities in the knowledge graph that correspond to the central entity type under consideration, establishing a central entity type property list for each of the found central type entities that comprises the found central type entity and other entities in the knowledge graph having a prescribed degree of relation to the central type entity under consideration, as well as the type of relation existing between the found central type entity and each of the other entities in the knowledge graph having a prescribed degree of relation to the central type entity under consideration, combining the central entity type property list established for the identified central entity types to produce a combined entity property list, and finding queries associated with entities listed in the combined entity property list in the query click log. 15. The system of claim 14, wherein the program module for inferring implicit relations from the found queries and generating an implicit relations data set comprising queries associated with the inferred implicit relations, comprises sub-modules for: for each of one or more of the found queries, using the query click log to identify from a found query the URL associated with a result presented from a search of the query that was selected by a user, determining if an entity associated with the identified URL is found in the query, wherein an entity is associated with a URL if the entity points to that URL in the knowledge graph, whenever the entity associated with the identified URL is not found in the query, using said combined entity property list to identify a central entity type related to the entity associated with the identified URL and what type of relation exists between that central entity type and the entity associated with the identified URL, and inferring the existence of an implicit relation from the found query and assigning the identified relation type to the entity associated with the identified URL; and generating an implicit relations data set having entries each of which comprises the text of a query associated with an inferred implicit relation as well as the type of relation assigned to the entity associated with the URL indentified from that query. 16. The system of claim 14, wherein the program module for inferring implicit relations from the found queries and generating an implicit relations data set comprising queries associated with the inferred implicit relations, comprises sub-modules for: for each of one or more of the found queries, using the query click log to identify from a found query the URL associated with a result presented from a search of the query that was selected by a user, determining if an entity associated with the identified URL is found in the query, wherein an entity is associated with a URL if the entity points to that URL in the knowledge graph, whenever the entity associated with the identified URL is not found in the query, using said combined entity property list to identify a central entity type related to the entity associated with the identified URL and determining if the identified the central entity type is found in the query, whenever the identified the central entity type is found in the query, identifying what type of relation exists between that central entity type and the entity associated with the identified URL, and inferring the existence of an implicit relation from the found query and assigning the identified relation type to the entity associated with the identified URL; and generating an implicit relations data set having entries each of which comprises the text of a query associated with an inferred implicit relation as well as the type of relation assigned to the entity associated with the URL indentified from that query. 17. The system of claim 13, wherein the program module for inferring implicit relations from the found queries and generating an implicit relations data set comprising queries associated with the inferred implicit relations, comprises sub-modules for: identifying, for one or more semantic entity relation types associated with a domain of interest, at least one entity pair in the knowledge graph having a first entity of the pair that is connected to another entity of the pair by a directed connector or path of connectors originating at the first entity by no more than the prescribed number of intermediate entities, and whose connector or connectors connecting the pair of entities have a relation label or labels that correspond to the semantic entity relation type associated with a domain of interest; determining, for each entity pair identified, if a found query contains the first entity of the pair, but not the other entity of the pair, whenever the found query contains the first entity of the pair, but not the other entity of the pair, using the query click log to identify from the found query the URL associated with a result presented from a search based on the query that was selected by a user, and determining if the other entity of the pair is associated with the identified URL, wherein an entity is associated with a URL if the entity points to that URL in the knowledge graph, whenever the other entity of the pair is associated with the identified URL, designating the found query infers an implicit relation, and assigning the semantic entity relation type associated with the domain of interest to said other entity of the entity pair, and assigning the relation of the first entity of the pair, if known, to that entity; and generating an implicit relations data set having entries each of which comprises the text of a query associated with an inferred implicit relation as well as the type of relation assigned to the first entity of an entity pair associated with the query and the type of relation assigned to said other entity of the entity pair. 18. The system of claim 13, wherein the program module for inferring implicit relations from the found queries and generating an implicit relations data set comprising queries associated with the inferred implicit relations, comprises sub-modules for: identifying, for one or more semantic entity relation types associated with a domain of interest, those found queries having the name of the relation type or a variation thereof contained therein, and at least one entity pair in the knowledge graph having a first entity of the pair that is connected to another entity of the pair by a directed connector or path of connectors originating at the first entity by no more than the prescribed number of intermediate entities, and whose connector or connectors connecting the pair of entities have a relation label or labels that correspond to the semantic entity relation type; determining, for each entity pair identified and each found query identified, if the query contains the first entity of the pair, but not the other entity of the pair, whenever the found query contains the first entity of the pair, but not the other entity of the pair, designating the found query infers an implicit relation, and assigning the semantic entity relation type associated with the domain of interest to said other entity of the entity pair, and assigning the relation of the first entity of the pair, if known, to that entity; and generating an implicit relations data set having entries each of which comprises the text of a query associated with an inferred implicit relation as well as the type of relation assigned to the first entity of an entity pair associated with the query and the type of relation assigned to said other entity of the entity pair. 19. The system of claim 13, further comprising a program module for inferring explicit relations from the found queries and generating an explicit relations data set comprising queries associated with the inferred explicit relations, and wherein the program module for training the semantic entity relation detection classifier comprises training the semantic entity relation detection classifier using the explicit and implicit data sets to find an explicit or implicit relation, or both, in a query. 20. A semantic entity relation detection classifier training system, comprising: one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices; and a computer program having program modules executable by the one or more computing devices, the one or more computing devices being directed by the program modules of the computer program to, receive a query click log and a knowledge graph, find queries included in the query click log that are associated with entities found in the knowledge graph, said entities being associated with a knowledge graph domain of interest, infer explicit relations from the found queries and generate an explicit relations data set comprising queries associated with the inferred explicit relations, infer implicit relations from the found queries and generate an implicit relations data set comprising queries associated with the inferred implicit relations, train a first classifier using the implicit relations data set to produce an implicit relations classifier that can find implicit relations in a query, apply the implicit relations classifier to each of the queries in the explicit relations data set to find queries predicted to have an implicit relation or implicit relations, augment the explicit relations data set, said augmenting comprising, for each query in the explicit relations data set predicted to have an implicit relation or implicit relations, adding the implicit relation or implicit relations predicted for that query to the explicit relations data set entry associated with the query to produce an augmented explicit relations data set, and train a second classifier using the augmented explicit relations data set to produce a combined relations classifier that can find explicit, or implicit relations, or both, in a query.