dc.contributor.advisor | Öztürk, Pinar | |
dc.contributor.advisor | Marsi, Erwin | |
dc.contributor.author | Næss, Sindre | |
dc.date.accessioned | 2019-09-11T10:55:57Z | |
dc.date.created | 2015-07-08 | |
dc.date.issued | 2015 | |
dc.identifier | ntnudaim:13587 | |
dc.identifier.uri | http://hdl.handle.net/11250/2615809 | |
dc.description.abstract | In the climate sciences, it is not feasible for a scientist to read more than a small
fraction of all the papers currently being published.
Our group is attempting to help climate scientists by making a tool for
discovering knowledge in scientific literature. Our approach involves the extraction
of causally related events involving either increase or decrease. To facilitate
reasoning and search, the relevant parts of the extracted variables need to be
identified and generalized. This is done by a combination of pruning the variables,
Named Entity Recognition, and using background knowledge about recognized
entities.
In the semantic web, an ever increasing amount of machine readable information
is becoming freely available. With growing support for Linked Data standards
and open licensing, there is now an abundance of structured data waiting to be
explored and utilized.
This study seeks to find and utilize knowledge bases in order to generalize
specific types of named entities, and finds that Linked Data resources are well
suited for this task. | en |
dc.language | eng | |
dc.publisher | NTNU | |
dc.subject | Datateknologi, Spillteknologi | en |
dc.title | Generalization of Named Entities - Using Linked Data | en |
dc.type | Master thesis | en |
dc.source.pagenumber | 51 | |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi og elektroteknikk,Institutt for datateknologi og informatikk | nb_NO |
dc.date.embargoenddate | 10000-01-01 | |