# Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph

arXiv: https://arxiv.org/abs/2204.10194 · HTML: https://mlsanity.com/paper/2204.10194/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1907.05774/) (2019)
- [TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph](https://mlsanity.com/paper/2104.07302/) (2021)
- [MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering](https://mlsanity.com/paper/2010.14095/) (2020)
- [Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1811.01118/) (2018)
- [Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2109.03614/) (2021)
- [Character-Level Question Answering with Attention](https://mlsanity.com/paper/1604.00727/) (2016)

## Built on by (most cited first, 9 of 9)

- [How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain](https://mlsanity.com/paper/2307.00186/) (2023)
- [Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking](https://mlsanity.com/paper/2304.13854/) (2023)
- [RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine](https://mlsanity.com/paper/2410.13987/) (2024)
- [GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health](https://mlsanity.com/paper/2601.18106/) (2026)
- [A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models](https://mlsanity.com/paper/2402.10779/) (2024)
- [Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs](https://mlsanity.com/paper/2511.04473/) (2025)
- [Benchingmaking Large Langage Models in Biomedical Triple Extraction](https://mlsanity.com/paper/2310.18463/) (2023)
- [RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents](https://mlsanity.com/paper/2605.26352/) (2026)
- [Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction](https://mlsanity.com/paper/2604.07659/) (2026)

## Abstract (arXiv)

Building query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its query graph is built correctly and the right answer is then retrieved by issuing the query graph against the KG. Therefore, this paper focuses on query graph generation from natural language questions. Existing approaches for query graph generation ignore the semantic structure of a question, resulting in a large number of noisy query graph candidates that undermine prediction accuracies. In this paper, we define six semantic structures from common questions in KGQA and develop a novel Structure-BERT to predict the semantic structure of a question. By doing so, we can first filter out noisy candidate query graphs, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to state-of-the-arts.