Abstract: A method, system, and program product for selecting software is provided. The method includes retrieving audio data during a call with a subject matter expert (SME). The audio data is converted into a data training set and documents of the SME are converted into a document training set. Canonical documents generated by authors are analyzed and specified code is extracted from the text data training set and document training set. Attributes of individuals are detected. The attributes are compared with specified data and the canonical documents and it is determined that the individuals are requesting information associated with the text data. The information is provided to the individuals via the canonical documents or the documents of the SME and it is determined if a matched set of data exists between the attributes, the specified data, and the canonical documents. A digital action associated with results of the determination is executed.

Abstract: A computer-implemented method, system and computer program product for improving query searches. After receiving a query from a user to conduct a content search, the query is analyzed for its semantic meaning and a categorized group of query tags and content tags in the central repository that is most semantically similar in meaning to the meaning of the query is identified. Furthermore, the content tags and query tags in the user's repository are analyzed to determine the interests of the user. The query may then be augmented to include one or more other terms of interest from the identified categorized group of query tags and content tags in the central repository that match the determined interests of the user within a threshold degree of relatedness, where these other terms of interest correspond to the content tags and query tags of the identified categorized group based on their assigned weight.

Abstract: A method, system, and program product for selecting software is provided. The method includes retrieving audio data during a call with a subject matter expert (SME). The audio data is converted into a data training set and documents of the SME are converted into a document training set. Canonical documents generated by authors are analyzed and specified code is extracted from the text data training set and document training set. Attributes of individuals are detected. The attributes are compared with specified data and the canonical documents and it is determined that the individuals are requesting information associated with the text data. The information is provided to the individuals via the canonical documents or the documents of the SME and it is determined if a matched set of data exists between the attributes, the specified data, and the canonical documents. A digital action associated with results of the determination is executed.

Abstract: A computer-implemented method, system and computer program product for improving query searches. After receiving a query from a user to conduct a content search, the query is analyzed for its semantic meaning and a categorized group of query tags and content tags in the central repository that is most semantically similar in meaning to the meaning of the query is identified. Furthermore, the content tags and query tags in the user's repository are analyzed to determine the interests of the user. The query may then be augmented to include one or more other terms of interest from the identified categorized group of query tags and content tags in the central repository that match the determined interests of the user within a threshold degree of relatedness, where these other terms of interest correspond to the content tags and query tags of the identified categorized group based on their assigned weight.