A Continual Relation Extraction Approach for Knowledge Graph Completeness
This work addresses the challenge of updating knowledge graphs with real-time data for information systems, though it appears incremental as it builds on existing relation extraction techniques.
The paper tackles the problem of extracting relations from streaming data to maintain knowledge graph completeness, specifically applying a novel continual relation extraction method to German and Austrian coronavirus news, achieving results that demonstrate improved accuracy in identifying entity interconnections.
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.