Abstract: The present invention provides a knowledge graph construction method and device. The method includes: obtaining structured data, where the structured data includes a first entity name of a first entity and attribute information corresponding to the first entity name, and the attribute information includes a first attribute and a first attribute value; performing, based on measurement of a similarity between the first entity and a second entity in a knowledge graph, entity alignment processing on the first entity, where the measurement of the similarity includes at least one of the following types: measurement of a character similarity, measurement of a structure similarity of a classification tree on which an entity is located, and measurement of an attribute similarity; and importing the structured data into the knowledge graph according to an entity alignment processing result. Embodiments may ensure correctness of data in the knowledge graph.

Abstract: Disclosed is a method, a device and equipment for fusing different instances describing the same entity. The method includes: acquiring a connection diagram comprising a plurality of instances, where different nodes in the connection diagram represent different instances, and connection lines between the nodes represent instance relations between the instances corresponding to the nodes; based on the instance relations, identifying different instances describing the same entity in the connection diagram, fusing the nodes corresponding to the identified instances, and updating the connection diagram.

Abstract: Disclosed is an information query method and apparatus. The method includes: obtaining a query word input by a user through a terminal, identifying a target entity in the query word and determining a globally unique identifier (GUID) of the target entity for a knowledge map; determining a target entity type corresponding to the target entity according to a corresponding relationship between an entity and the entity type. The target entity type is used to indicate the target attribute and/or target entity relationship to be queried of the target entity; according to the GUID and an identifier of the target attribute and/or an identifier of the target entity relationship, querying an attribute value and/or a related entity corresponding to the identity of the target attribute and/or an identifier of the target entity relationship in the knowledge map; returning a search result to the terminal.

Abstract: Knowledge map-based question-answer method, server, and storage medium are provided. The method includes obtaining a natural query sentence inputted by a user, and identifying a globally unique identifier, with respect to the knowledge map, of an entity in the natural query sentence. The knowledge map includes attributes and attribute values of entities and relationships thereof. The method also includes according to a context-free grammar rule, parsing the natural query sentence into a syntax tree, and according to the syntax tree, obtaining a logical expression corresponding to the natural query sentence. Moreover, the method includes according to the logical expression and the globally unique identifier of the entity, generating a machine query sentence corresponding to the knowledge map.

Abstract: Disclosed is an information query method and apparatus. The method includes: obtaining a query word input by a user through a terminal, identifying a target entity in the query word and determining a globally unique identifier (GUID) of the target entity for a knowledge map; determining a target entity type corresponding to the target entity according to a corresponding relationship between an entity and the entity type. The target entity type is used to indicate the target attribute and/or target entity relationship to be queried of the target entity; according to the GUID and an identifier of the target attribute and/or an identifier of the target entity relationship, querying an attribute value and/or a related entity corresponding to the identity of the target attribute and/or an identifier of the target entity relationship in the knowledge map; returning a search result to the terminal.

Abstract: The present invention provides a knowledge graph construction method and device. The method includes: obtaining structured data, where the structured data includes a first entity name of a first entity and attribute information corresponding to the first entity name, and the attribute information includes a first attribute and a first attribute value; performing, based on measurement of a similarity between the first entity and a second entity in a knowledge graph, entity alignment processing on the first entity, where the measurement of the similarity includes at least one of the following types: measurement of a character similarity, measurement of a structure similarity of a classification tree on which an entity is located, and measurement of an attribute similarity; and importing the structured data into the knowledge graph according to an entity alignment processing result. Embodiments may ensure correctness of data in the knowledge graph.

Abstract: Knowledge map-based question-answer method, server, and storage medium are provided. The method includes obtaining a natural query sentence inputted by a user, and identifying a globally unique identifier, with respect to the knowledge map, of an entity in the natural query sentence. The knowledge map includes attributes and attribute values of entities and relationships thereof. The method also includes according to a context-free grammar rule, parsing the natural query sentence into a syntax tree, and according to the syntax tree, obtaining a logical expression corresponding to the natural query sentence. Moreover, the method includes according to the logical expression and the globally unique identifier of the entity, generating a machine query sentence corresponding to the knowledge map.

Abstract: Disclosed is a method, a device and equipment for fusing different instances describing the same entity. The method includes: acquiring a connection diagram comprising a plurality of instances, where different nodes in the connection diagram represent different instances, and connection lines between the nodes represent instance relations between the instances corresponding to the nodes; based on the instance relations, identifying different instances describing the same entity in the connection diagram, fusing the nodes corresponding to the identified instances, and updating the connection diagram.