Abstract: The present disclosure relates to a computer-implemented method of verifying a semantic triple generated for building a knowledge base including data patterns defining concepts associated with semantic triples derived from unstructured text. The method includes providing the semantic triple to a user interface, the semantic triple including a subject, an object, and a relation. The method also includes receiving, from the user interface, an acceptance or a rejection of the subject, the object, and the relation as relevant or not to the knowledge base. The method also includes transmitting the semantic triple for inclusion as a data pattern in the knowledge base in the event that all of the subject, the object, and the relation, have been accepted.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: The subject-matter described herein relates to a computer-implemented method of enabling interoperability between a first knowledge base and a second knowledge base. Each knowledge base is graphically represented and includes a plurality of nodes each defining a concept and a plurality of relations linking the plurality of nodes. The first knowledge base and the second knowledge base are encoded using different coding standards. The method comprises: identifying an entity from the plurality of entities in the second knowledge base; obtaining a mapping between the identified entity from the second knowledge base and a matching entity from the first knowledge base; and creating and storing a link between the identified entity from the second knowledge base and the matching entity from the first knowledge base.

Abstract: A text processing method for improving the accuracy of a response to a query directed to a system comprising concepts and relations defined by a knowledge base, wherein the method comprises: (i) producing a dependency tree from the query, wherein the dependency tree has at least one branch containing nodes and at least one connection between those nodes, wherein each node has a node label which corresponds to a term within the query, and wherein each connection has a label which corresponds to the linguistic relationship between terms within the query; (ii) from the dependency tree, generating a query concept using concepts and relations defined by the knowledge base; (iii) checking if the query concept has a subsumption relationship with a candidate concept retrieved from the system, and if no subsumption relationship is initially identified, optimising the dependency tree by changing the nodes, followed by repeating steps (ii) and (iii); and wherein the query concept and the candidate concept comprise at

Abstract: Methods for reducing the number of potential matches of entries in a database to a user inputted query are provided. In one aspect, a method includes receiving a user inputted query, identifying a plurality of candidate entries in said database that provide a match to said user inputted query, and grouping the plurality of candidate entries on the basis of their associated semantic type. The method also includes selecting the group with the largest number of entries, and transmitting a request to a user to select between the entries in the group with the largest number of entries. Systems and machine-readable media are also provided.

Abstract: The present disclosure relates to a computer-implemented method of verifying a semantic triple generated for building a knowledge base including data patterns defining concepts associated with semantic triples derived from unstructured text. The method includes providing the semantic triple to a user interface, the semantic triple including a subject, an object, and a relation. The method also includes receiving, from the user interface, an acceptance or a rejection of the subject, the object, and the relation as relevant or not to the knowledge base. The method also includes transmitting the semantic triple for inclusion as a data pattern in the knowledge base in the event that all of the subject, the object, and the relation, have been accepted.

Abstract: A text processing method for improving the accuracy of a response to a query directed to a system comprising concepts and relations defined by a knowledge base, wherein the method comprises: (i) producing a dependency tree from the query, wherein the dependency tree has at least one branch containing nodes and at least one connection between those nodes, wherein each node has a node label which corresponds to a term within the query, and wherein each connection has a label which corresponds to the linguistic relationship between terms within the query; (ii) from the dependency tree, generating a query concept using concepts and relations defined by the knowledge base; (iii) checking if the query concept has a subsumption relationship with a candidate concept retrieved from the system, and if no subsumption relationship is initially identified, optimising the dependency tree by changing the nodes, followed by repeating steps (ii) and (iii); and wherein the query concept and the candidate concept comprise at

Abstract: The present disclosure relates to a computer-implemented method of generating a semantic triple for building a knowledge base to include data patterns associated with semantic triples derived from unstructured text. The method includes providing a sentence associated with unstructured text including a main verb and a taxonomic verb, generating a first frame, generating a second frame, identifying a common sub-string in the first subject and either the second subject or the second object, or a common sub-string in the first object and either the second subject or the second object; generating a semantic triple using the first frame and replacing the first subject or the first object having the common sub-string with the second subject or the second object having the common sub-string; and transmitting the semantic triple for inclusion as a data pattern in the knowledge base.