The ALTER-NATIVA knowledge management approach

Nowadays, it is commonly known that information systems need an agile capability of handling knowledge. To accomplish this, systems have to have a formal knowledge representation ability supported by specific and advanced reasoning features. This research work proposes a knowledge management approach with the purpose to gather, model and consume community knowledge for specific recommendation commitments. Such approach is accomplished by a semantic lexicon alignment between the various community knowledge assets, to facilitate collaborations establishment between people and systems in an interoperable fashion. Thus, a knowledge base supported by a thesaurus able to represent all the metadata needed to represent and characterize the various community stakeholders’ resources is proposed. The thesaurus represents the lexicon in the domain, which in the ALTER-NATIVA systems is mostly used to support the various e-Learning elements (e.g. courses) and users categorization, sustained by synchronization features to facilitate a constant update of its information. A set of services designed to recommend specific resources in relation to a determined profile of user is provided. Additionally, a discussion about how the ALTER-NATIVA knowledge management approach can be applied to industrial environments is presented.     

Informed Recommender: Basing Recommendations on Consumer Product Reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.