From Knowledge Organization to Knowledge Representation

So far, within the Library and Information Science (LIS) community, Knowledge Organization (KO) has developed its own very successful solutions to document search, allowing for the classification and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the Artificial Intelligence and Semantic Web communities, Knowledge Representation (KR), has developed very powerful end expressive techniques which, via the use of ontologies, support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology, inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.