Abstract: Methods and apparatuses are described for building a knowledge base taxonomy from structured or unstructured computer text for use in automated user interactions. A server computing device receives one or more of structured text or unstructured text corresponding to historical user interaction data from a database. The server computing device extracts one or more terms from the received text that are most relevant to a subject matter domain. The server computing device organizes the extracted one or more terms into a taxonomy data structure.

Abstract: Methods and apparatuses are described for building a knowledge base taxonomy from structured or unstructured computer text for use in automated user interactions. A server computing device receives one or more of structured text or unstructured text corresponding to historical user interaction data from a database. The server computing device extracts one or more terms from the received text that are most relevant to a subject matter domain. The server computing device organizes the extracted one or more terms into a taxonomy data structure.

Abstract: A semantic data resource of a domain is processed by calculating relevance scores for terms which occur in domain corpora and weighting the semantic data resource depending on the relevance scores calculated for these terms. The semantic data resource may include domain-specific terms and relations, such as a domain ontology, a domain terminology and a domain classification. The domain ontology may include a domain-specific-hierarchy of terms assigned to nodes which are connected by edges and may be encoded in a web ontology language. The relevance scores may be chi-square scores which are calculated depending on a frequency of a term in the domain corpora and an expected frequency of the term.