# BERTologyNavigator: Advanced Question Answering with BERT-based Semantics

arXiv: https://arxiv.org/abs/2401.09553 · HTML: https://mlsanity.com/paper/2401.09553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 16 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://mlsanity.com/paper/2010.12412/) (2020)
- [A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization](https://mlsanity.com/paper/1902.01069/) (2019)
- [Few-shot In-context Learning for Knowledge Base Question Answering](https://mlsanity.com/paper/2305.01750/) (2023)
- [Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments](https://mlsanity.com/paper/2212.09736/) (2022)
- [ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering](https://mlsanity.com/paper/2204.08109/) (2022)
- [One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones](https://mlsanity.com/paper/2202.07028/) (2022)
- [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022)
- [DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2303.13351/) (2023)
- [DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2309.07545/) (2023)

## Built on by (most cited first, 2 of 2)

- [SPARQL Generation with Entity Pre-trained GPT for KG Question Answering](https://mlsanity.com/paper/2402.00969/) (2024)
- [SDEC: Semantic Deep Embedded Clustering](https://mlsanity.com/paper/2508.15823/) (2025)

## 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.