TY - GEN
T1 - Enhancing enterprise knowledge processes via cross-media extraction
AU - Iria, Jose
AU - Uren, Victoria
AU - Lavelli, Alberto
AU - Blohm, Sebastian
AU - Dadzie, Aba-sah
AU - Franz, Thomas
AU - Kompatsiaris, Ioannis
AU - Magalhaes, Joao
AU - Nikolopoulos, Spiros
AU - Preisach, Christine
AU - Slavazza, Piercarlo
PY - 2007/10/28
Y1 - 2007/10/28
N2 - In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.
AB - In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.
UR - http://dl.acm.org/citation.cfm?doid=1298406.1298441
U2 - 10.1145/1298406.1298441
DO - 10.1145/1298406.1298441
M3 - Conference publication
SN - 978-1-59593-643-1
SP - 175
EP - 176
BT - K-CAP '07 - proceedings of the 4th international conference on Knowledge capture
A2 - Sleeman, Derek
A2 - Barker, Ken
PB - ACM
CY - New York, NY (US)
T2 - 4th International Conference on Knowledge Capture
Y2 - 28 October 2007 through 31 October 2007
ER -