# Knowledge Graph Question Answering via SPARQL Silhouette Generation

arXiv: https://arxiv.org/abs/2109.09475 · HTML: https://mlsanity.com/paper/2109.09475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases](https://mlsanity.com/paper/2004.03125/) (2020)
- [Improved Neural Relation Detection for Knowledge Base Question Answering](https://mlsanity.com/paper/1704.06194/) (2017)
- [Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering](https://mlsanity.com/paper/1607.06275/) (2016)
- [Lambda Dependency-Based Compositional Semantics](https://mlsanity.com/paper/1309.4408/) (2013)
- [Message Passing for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1908.06917/) (2019)
- [An Encoder-Decoder Framework Translating Natural Language to Database Queries](https://mlsanity.com/paper/1711.06061/) (2017)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach](https://mlsanity.com/paper/1804.08798/) (2018)
- [Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1811.01118/) (2018)
- [Towards a Question Answering System over the Semantic Web](https://mlsanity.com/paper/1803.00832/) (2018)
- [CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases](https://mlsanity.com/paper/1606.01994/) (2016)
- [Simple Question Answering by Attentive Convolutional Neural Network](https://mlsanity.com/paper/1606.03391/) (2016)
- [Pretrained Transformers for Simple Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2001.11985/) (2020)
- [Character-Level Question Answering with Attention](https://mlsanity.com/paper/1604.00727/) (2016)
- [Weakly-supervised Neural Semantic Parsing with a Generative Ranker](https://mlsanity.com/paper/1808.07625/) (2018)

## Built on by (most cited first, 1 of 1)

- [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026)

## Abstract (arXiv)

Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge Graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two-stage neural architecture to solve the KGQA task. The first stage generates a sketch of the target SPARQL called SPARQL silhouette for the input question. This comprises of (1) Noise simulator to facilitate out-of-vocabulary words and to reduce vocabulary size (2) seq2seq model for text to SPARQL silhouette generation. The second stage is a Neural Graph Search Module. SPARQL silhouette generated in the first stage is distilled in the second stage by substituting precise relation in the predicted structure. We simulate ideal and realistic scenarios by designing a noise simulator. Experimental results show that the quality of generated SPARQL silhouette in the first stage is outstanding for the ideal scenarios but for realistic scenarios (i.e. noisy linker), the quality of the resulting SPARQL silhouette drops drastically. However, our neural graph search module recovers it considerably. We show that our method can achieve reasonable performance improving the state-of-art by a margin of 3.72% F1 for the LC-QuAD-1 dataset. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are suited for practical applications.