dc.identifier.uri |
http://dx.doi.org/10.15488/12854 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/12958 |
|
dc.contributor.author |
Hrou, Moussab
|
eng |
dc.date.accessioned |
2022-10-13T06:36:52Z |
|
dc.date.available |
2022-10-13T06:36:52Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Hrou, Moussab: Evaluating SQuAD-based Question Answering for the Open Research Knowledge Graph Completion. Hannover : Gottfried Wilhelm Leibniz Universität Hannover, Bachelor Thesis, 2022, IX, 48 S. DOI: https://doi.org/10.15488/12854 |
eng |
dc.description.abstract |
Every year, approximately around 2.5 million new scientific papers are published.
With the rapidly growing publication trends, it is increasingly difficult to manually
sort through and keep track of the relevant research – a problem that is only more
acute in a multidisciplinary setting. The Open Research Knowledge Graph (ORKG)
is a next-generation scholarly communication platform that aims to address this
issue by making knowledge about scholarly contributions machine-actionable, thus
enabling completely new ways of human-machine assistance in comprehending re-
search progress.
As such, the ORKG is powered by a diverse spectrum of NLP services to assist the
expert users in structuring scholarly contributions and searching for the most rele-
vant contributions. For a prospective recommendation service, this thesis examines
the task of automated ORKG completion as an object extraction task from a given
paper Abstract for a query ORKG predicate. As a main contribution of this thesis,
automated ORKG completion is formulated as an extractive Question Answering
(QA) machine learning objective under an open world assumption. Specifically, the
task attempted in this work is fixed-prompt Language Model (LM) tuning (LMT)
for few-shot ORKG object prediction formulated as the well-known SQuAD extrac-
tive QA objective. Three variants of BERT-based transfomer LMs are evaluated.
To support the novel LMT task, this thesis introduces a scholarly QA dataset akin
in characteristics to the SQuAD QA dataset generated semi-automatically from the
ORKG knowledge base. As a result, the BERT model variants when tested in vanilla setting versus after LMT, show a positive, significant performance uplift for auto-mated ORKG completion as an object completion task. This thesis offers a strong empirical basis for future research aiming at a production-ready automated ORKG completion model. |
eng |
dc.language.iso |
eng |
eng |
dc.publisher |
Hannover : Gottfried Wilhelm Leibniz Universität Hannover |
|
dc.rights |
CC BY 3.0 DE |
eng |
dc.rights.uri |
http://creativecommons.org/licenses/by/3.0/de/ |
eng |
dc.subject |
Question Answering |
eng |
dc.subject |
Link Prediction |
eng |
dc.subject |
Open Research Knowledge Graph |
eng |
dc.subject |
Prompt-based Learning |
eng |
dc.subject |
Knowledge Graph Completion |
eng |
dc.subject.ddc |
004 | Informatik
|
eng |
dc.title |
Evaluating SQuAD-based Question Answering for the Open Research Knowledge Graph Completion |
eng |
dc.type |
BachelorThesis |
eng |
dc.type |
Text |
eng |
dc.description.version |
publishedVersion |
eng |
tib.accessRights |
frei zug�nglich |
eng |