Abstract: A non-transitory computer-readable recording medium storing a training data generation program for causing a computer to execute processing including: identifying, from among meta-analysis literatures stored in a memory, a plurality of meta-analysis literatures in which a first literature is cited; determining a degree of similarity between the plurality of identified meta-analysis literatures based on feature information of the plurality of identified meta-analysis literatures; and in response to the degree of similarity being equal to or higher than a threshold, generating training data for machine learning including the first literature.

Abstract: An apparatus links an entity in a first knowledge-graph with a word in a text. The apparatus, based on a number of first-edges coupled to each of first-nodes serving as a transition-source and a number of second-edges coupled to each of second-nodes serving as a transition-destination in the first knowledge-graph, identifies a third-edge to be deleted from edges coupled to a third-node among the second-nodes which has a preset input-order indicating a number of edges that transition to the third-node, and generates a second knowledge-graph by deleting the third-edge from the first knowledge-graph. The apparatus couples first and second nodes which have been coupled to each other by the third-edge in the first knowledge-graph, via a fourth-node to which the first and second nodes are coupled by edges in the second knowledge-graph, and provides the word in the text and the entity linked with the word to a user.

Abstract: A non-transitory computer-readable recording medium storing a training data generation program for causing a computer to execute processing including: identifying, from among meta-analysis literatures stored in a memory, a plurality of meta-analysis literatures in which a first literature is cited; determining a degree of similarity between the plurality of identified meta-analysis literatures based on feature information of the plurality of identified meta-analysis literatures; and in response to the degree of similarity being equal to or higher than a threshold, generating training data for machine learning including the first literature.

Abstract: A training apparatus obtains RDF data including subjects, predicates, and objects, inputs a subject, a predicate, and an object of a first record of the obtained RDF data, and determines a predicate of a second record that has been previously input, the predicate having the same character string as the subject or object of the first record. The training apparatus generates training data including RDF data having the subject or object of the first record and the predicate of the second record that have been associated with each other. The training apparatus performs training for the generated training data so that a vector resulting from addition of a vector of the predicate to a vector of the subject associated with the RDF data becomes closer to a vector of the object associated with the RDF data, and thereby enables improvement in prediction accuracy for searching through the RDF data.

Abstract: An apparatus links an entity in a first knowledge-graph with a word in a text. The apparatus, based on a number of first-edges coupled to each of first-nodes serving as a transition-source and a number of second-edges coupled to each of second-nodes serving as a transition-destination in the first knowledge-graph, identifies a third-edge to be deleted from edges coupled to a third-node among the second-nodes which has a preset input-order indicating a number of edges that transition to the third-node, and generates a second knowledge-graph by deleting the third-edge from the first knowledge-graph. The apparatus couples first and second nodes which have been coupled to each other by the third-edge in the first knowledge-graph, via a fourth-node to which the first and second nodes are coupled by edges in the second knowledge-graph, and provides the word in the text and the entity linked with the word to a user.

Abstract: A non-transitory computer-readable recording medium stores therein a query generation program that causes a computer to execute a process including: determining, from a plurality of category schemas each being a set of attribute schemas, a main category schema to be associated with an output target table that defines acquisition target data, based on attribute names of a plurality of output items contained in the output target table; determining a subcategory schema to be associated with an un-associated item, which is an output item that is not associated with the main category schema among the plurality of output items, from category schemas that are associated with respective features of a plurality of attribute schemas contained in the main category schema; and generating queries with respect to the plurality of category schemas based on the main category schema and the subcategory schema.

Abstract: A non-transitory computer-readable recording medium stores therein a query generation program that causes a computer to execute a process including: determining, from a plurality of category schemas each being a set of attribute schemas, a main category schema to be associated with an output target table that defines acquisition target data, based on attribute names of a plurality of output items contained in the output target table; determining a subcategory schema to be associated with an un-associated item, which is an output item that is not associated with the main category schema among the plurality of output items, from category schemas that are associated with respective features of a plurality of attribute schemas contained in the main category schema; and generating queries with respect to the plurality of category schemas based on the main category schema and the subcategory schema.