BERTologyNavigator: Advanced Question Answering with BERT-based Semantics
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Abstract (arXiv)
The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing. In this study, we introduce the BERTologyNavigator -- a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge Graph (KG). Our approach focuses on extracting one-hop relations and labelled candidate pairs in the first phases. This is followed by employing BERT's CLS embeddings and additional heuristics for relation selection in the second phase. Our system reaches an F1 score of 0.2175 on the DBLP QuAD Final test dataset for Scholarly QALD and 0.98 F1 score on the subset of the DBLP QuAD test dataset during the QA phase.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 16 references that are in the map. First posted on arXiv 17 Jan 2024.
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15 of 16 references.
References · 15
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.
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- 2025SDEC: Semantic Deep Embedded Clusteringcited by 0
- 2025SDEC: Semantic Deep Embedded Clustering18 Aug 2025
- 2024SPARQL Generation with Entity Pre-trained GPT for KG Question Answering1 Feb 2024