Breaking free from your information prison: A recommender based on semantically enriched context descriptions

• Published in: Proceedings of the First International Conference on Enterprise Systems: ES 2013 pp. 1 - 9
• Main Authors: Lutz, Jonas, Thonssen, Barbara, Witschel, Hans Friedrich
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2013
• Subjects: Abstracts; Context; Information management; Information services; machine learning; Ontologies; Portals; similarity
• DOI: 10.1109/ES.2013.6690092

Information repositories, implemented as Enterprise Portals (EP) on the intranet, are increasingly popular in companies of all sizes. Enterprise Portals allow for structuring information in a way that resembles the organization of paper copies, i.e. simulating folders and registries and furthermore, provide simple routines for publishing and collaborating. Hence, in general, such kind of information management is not much different from paper management: electronic documents must be uploaded into the Enterprise Portal manually, filed into folders (which have to be created manually, too), tagged and related to other information objects if need be. With this approach information structuring remains subject to the individual user leading to the well-known problems of multiple filing, overlooking relevant information and incomprehensible folder structure. The SEEK!sem project aims at improving such kind of information system by automatically identifying and recommending related information resources to be added to a folder. The recommendations are based on rules, exploiting content and context similarity of information resources. Rules can be created upfront, based on explicitly defined relations between information objects. They can also be machine learned, i.e. the recommender exploits the existing linkage between documents, folders and other objects to learn "relatedness rules". In either case, potential new connections are inferred by applying the rules in a reasoning step. Recommended new connections are ranked by the sum of the scores of all applied rules - the rule scores, again, can either be provided by experts or machine-learned. The applied rules can serve as an explanation of a recommendation, i.e. they can assist users in understanding why a particular connection is suggested.