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.
|Title of host publication||K-CAP '07 - proceedings of the 4th international conference on Knowledge capture|
|Editors||Derek Sleeman, Ken Barker|
|Place of Publication||New York, NY (US)|
|Number of pages||2|
|Publication status||Published - 28 Oct 2007|
|Event||4th International Conference on Knowledge Capture - Whistler, BC, Canada|
Duration: 28 Oct 2007 → 31 Oct 2007
|Conference||4th International Conference on Knowledge Capture|
|Period||28/10/07 → 31/10/07|
Iria, J., Uren, V.
, Lavelli, A., Blohm, S., Dadzie, A., Franz, T., Kompatsiaris, I., Magalhaes, J., Nikolopoulos, S., Preisach, C., & Slavazza, P. (2007). Enhancing enterprise knowledge processes via cross-media extraction
. In D. Sleeman, & K. Barker (Eds.), K-CAP '07 - proceedings of the 4th international conference on Knowledge capture
(pp. 175-176). ACM. https://doi.org/10.1145/1298406.1298441