Abstract: A method, a system, and a computer program product for generating name recommendations in a core data services computing environment. A dataset for training a name data model is received. The name data model is configured for determination of a recommendation for one or more names in a plurality of names associated with one or more artifacts in a plurality of artifacts of a database management system. The name data model is trained using the received dataset and applied to generate one or more names associated with the one or more artifacts.

Abstract: Computerized systems for automating content annotation (e.g., tag creation and/or expansion) for low-content items within a computer network by leveraging intelligence of other data sources within a network to generate secondary content (e.g., a “context”) for items (e.g., documents) for use in a tagging process. For example, based on user assigned tags for an item, secondary content information can be generated and used to determine a new list of candidate tags for the item. Additionally, the context of an input item may be compared against the respective contexts of a plurality of other items to determine respective levels of similarity between the input item and each of the plurality of other items in order to annotate the input item. Techniques involving web-distance based clustering and leveraging crowd-sourced information sources to remove noisy data from annotated results are also described.

Abstract: Computerized systems for automating content annotation (e.g., tag creation and/or expansion) for low-content items within a computer network by leveraging intelligence of other data sources within a network to generate secondary content (e.g., a “context”) for items (e.g., documents) for use in a tagging process. For example, based on user assigned tags for an item, secondary content information can be generated and used to determine a new list of candidate tags for the item. Additionally, the context of an input item may be compared against the respective contexts of a plurality of other items to determine respective levels of similarity between the input item and each of the plurality of other items in order to annotate the input item. Techniques involving web-distance based clustering and leveraging crowd-sourced information sources to remove noisy data from annotated results are also described.

Abstract: Computerized systems for providing interest-to-item matching when item metadata is lacking or unavailable such that desired items of interest (e.g., research datasets) may be located for a user. For instance, the computing system may generate a context of a user's interest based on information indicating the user's interest (e.g., authors of research document, title of research document), and use the context to identify potentially relevant items and determine the relevance of the items to the user's interest. Additionally, a searchable database of items is generated by extracting identifiers of low content items from publicly available sources, such as the Internet, and generating contexts for the identified items. The computing system then indexes the identified items in the database using the generated contexts thereby enabling users to search the database for items of interest. Moreover, generating a context for items provides better accessibility for items that have little or no indexable content (e.g.