Abstract: A database system may generate a new case associated with a customer support query. The database system may initiate a search for other cases related to the new case and identify articles linked to the related cases. The database system may identify more relevant articles by finding the related cases that use a similar vocabulary for describing similar customer problems. The database system then identifies the articles used for resolving the prior problems. Thus, the database system may bridge the gap between vocabularies used by customers to describe problems and vocabularies used in articles to describe solutions to those problems.

Abstract: A database system may generate a new case associated with a customer support query. The database system may initiate a search for other cases related to the new case and identify articles linked to the related cases. The database system may identify more relevant articles by finding the related cases that use a similar vocabulary for describing similar customer problems. The database system then identifies the articles used for resolving the prior problems. Thus, the database system may bridge the gap between vocabularies used by customers to describe problems and vocabularies used in articles to describe solutions to those problems.

Abstract: Semantic-based processing techniques are disclosed. Semantic processing of content data comprising a content item is performed. A semantic processing-triggered event is triggered based at least in part on the semantic processing. A responsive action is performed in response to the event. The responsive action may include launching a business process, providing a result of semantic processing as an input, or augmenting a content item metadata set with a result of semantic processing.

Abstract: The invention is a system and method for teaching and using a computer to apply a genetic algorithm in order to generate rules for classifying data objects, such as documents. By permitting periodic human intervention during and/or after the initial training stage, the invention improves upon existing methods that merely automate data classification. Using an initial “training set” of data objects, the system develops a set of rules to apply on future data objects. To improve efficacy over time, rules may be periodically amended by a user during the initial training stage or after rules have been generated. In this fashion, classification rules may evolve, thereby improving the accuracy and speed of the classification process. Classification rules are also presented in a human-readable format, thereby improving the transparency of the classification process and facilitating the interaction between the system and the administrator.