A Continual Relation Extraction Approach for Knowledge Graph Completeness
<|reference_start|>A Continual Relation Extraction Approach for Knowledge Graph Completeness: 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.<|reference_end|>
@article{efeoglu2024a,
    title={A Continual Relation Extraction Approach for Knowledge Graph
  Completeness},
    author={Sefika Efeoglu},
    journal={TPDL 2022: 26th International Conference on Theory and Practice of
  Digital Libraries, 20-23 September 2022, Padua, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17593},
    primaryClass={cs.DL cs.AI}
}