Semantic query answering in digital libraries

Authors:
Ilianna Kollia;Kostas Rapantzikos;Giorgos Stamou;Andreas Stafylopatis
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

Citing 6
Cited 0

The Description Logic Handbook

The Description Logic Handbook
Statistical Learning for Inductive Query Answering on OWL Ontologies

ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
SPARQL query answering over OWL ontologies

ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Overview of the MPEG-7 standard

IEEE Transactions on Circuits and Systems for Video Technology
A systemic approach for effective semantic access to cultural content

Semantic Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.