Abstract: A triple verification method is provided.

Abstract: Provided is an incorrect triple detection system including a triple selector configured to select a target triple (subject, type, object) in a knowledge base, a sampler configured to create a sentence model by connecting object triples sharing entities included in the target triple, a model builder configured to embed the sentence model into a vector space to create a training entity vector and build an embedding model, and an incorrect triple detector configured to detect an incorrect triple by inputting a test triple into the embedding model.

Abstract: A triple verification method is provided.

Abstract: Provided is an incorrect triple detection system including a triple selector configured to select a target triple (subject, type, object) in a knowledge base, a sampler configured to create a sentence model by connecting object triples sharing entities included in the target triple, a model builder configured to embed the sentence model into a vector space to create a training entity vector and build an embedding model, and an incorrect triple detector configured to detect an incorrect triple by inputting a test triple into the embedding model.

Abstract: Disclosed are ontology-based reasoning apparatus, system, and method using user-defined rules. The ontology-based reasoning apparatus includes a memory storing two or more triple data; and a processor connected to the memory, in which the processor parses user-defined rules input from a user in ontology-based rules to generate a plurality of condition meta information corresponding to a plurality of conditions included in a condition section of the user-defined rules and one result meta information corresponding to one result included in a conclusion section of the user-defined rules, retrieves a plurality of triple data in two or more triple data prestored using the plurality of condition meta information, and reasons new triple data from the plurality of triple data retrieved using the result meta information to store the reasoned new triple data in the memory.

Abstract: Disclosed are a method of operating a knowledgebase and a server using the same. The method of operating a knowledgebase may include: (a) receiving triple data sets as input; (b) forming at least one data cluster set by classifying the triple data sets according to relation based on semantic information of the knowledgebase; and (c) learning each relation model by inputting the data cluster set into a neural tensor network.

Abstract: Ontology reasoning method and apparatus are disclosed. The ontology reasoning method comprises (a) broadcasting a schema triple to nodes; (b) partitioning triples other than the schema triple and distributing the partitioned triples to the nodes; and (c) reasoning at least one of the schema triple or the triples according to a reasoning rule and then renewing a confidence value of the reasoned triple.