A Continual Relation Extraction Approach for Knowledge Graph Completeness
Not on the map. Why?
Abstract (arXiv)
Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be converted into Knowledge Graphs, by leveraging an information extraction pipeline whose main tasks are named entity recognition and relation extraction. This thesis aims to develop a novel continual relation extraction method to identify relations (interconnections) between entities in a data stream coming from the real world. Domain-specific data of this thesis is corona news from German and Austrian newspapers.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 20 Apr 2024.
Builds on
11 of 16 references.
References · 11
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
- 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
- 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
- 2020Continual Lifelong Learning in Natural Language Processing: A Surveycited by 152
- 2017Relation Extraction : A Surveycited by 44
- 2021Curriculum-Meta Learning for Order-Robust Continual Relation Extractioncited by 34
- 2019Neural Snowball for Few-Shot Relation Learningcited by 22
- 2018SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approachcited by 18
- 2020Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervisioncited by 12
- 2020Knowledge Graphs and Knowledge Networks: The Story in Briefcited by 11
Built on by
Not cited yet.