# Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

arXiv: https://arxiv.org/abs/1808.04126 · HTML: https://mlsanity.com/paper/1808.04126/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 30 of 30)

- [QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers](https://mlsanity.com/paper/2202.00120/) (2022)
- [FIND: A Function Description Benchmark for Evaluating Interpretability Methods](https://mlsanity.com/paper/2309.03886/) (2023)
- [SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions](https://mlsanity.com/paper/2407.11417/) (2024)
- [Exploring Edge Disentanglement for Node Classification](https://mlsanity.com/paper/2202.11245/) (2022)
- [Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks](https://mlsanity.com/paper/2107.10234/) (2021)
- [MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation](https://mlsanity.com/paper/2307.07832/) (2023)
- [MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation](https://mlsanity.com/paper/2406.18815/) (2024)
- [Quantifying Adaptability in Pre-trained Language Models with 500 Tasks](https://mlsanity.com/paper/2112.03204/) (2021)
- [Towards Faithful and Consistent Explanations for Graph Neural Networks](https://mlsanity.com/paper/2205.13733/) (2022)
- [A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/2201.05793/) (2022)
- [RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks](https://mlsanity.com/paper/2307.07840/) (2023)
- [Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering](https://mlsanity.com/paper/2403.02966/) (2024)
- [SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases](https://mlsanity.com/paper/2109.13430/) (2021)
- [LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation](https://mlsanity.com/paper/2407.15351/) (2024)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment](https://mlsanity.com/paper/2301.02791/) (2023)
- [SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15272/) (2024)
- [Learning to Transpile AMR into SPARQL](https://mlsanity.com/paper/2112.07877/) (2021)
- [PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation](https://mlsanity.com/paper/2410.22623/) (2024)
- [Modeling Global Semantics for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2101.01510/) (2021)
- [ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models](https://mlsanity.com/paper/2504.07624/) (2025)
- [Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata](https://mlsanity.com/paper/2305.14202/) (2023)
- [Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems](https://mlsanity.com/paper/2601.00274/) (2026)
- [Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks](https://mlsanity.com/paper/2106.13061/) (2021)
- [Semantic Answer Type and Relation Prediction Task (SMART 2021)](https://mlsanity.com/paper/2112.07606/) (2021)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [Calculating Question Similarity is Enough: A New Method for KBQA Tasks](https://mlsanity.com/paper/2111.07658/) (2021)
- [Knowledge Graph Quality Evaluation under Incomplete Information](https://mlsanity.com/paper/2212.00994/) (2022)
- [KoRe: Compact Knowledge Representations for Large Language Models](https://mlsanity.com/paper/2605.20170/) (2026)
- [KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval](https://mlsanity.com/paper/2607.27136/) (2026)

## Abstract (arXiv)

The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.