Semantic query answering in digital libraries

A large activity for digitization, access and preservation of cultural heritage is taking place in Europe and the United States, which involves all types of cultural institutions, i.e., galleries, libraries, museums, archives and all types of cultural content. Semantic interoperability is a key issue in these developments. Content metadata constitute the main features of cultural items that are analysed and used to interpret users' queries, so that the most appropriate content is presented to the users. This paper presents a new semantic search methodology, including a query answering mechanism which meets the semantics of users' queries and enriches the answers by exploiting appropriate visual features, through an interweaved knowledge and machine learning based approach. An experimental study is presented, using content from the Europeana digital library, illustrating the improved performance of the proposed semantic search approach.