# Semantic Parsing for Question Answering over Knowledge Graphs

arXiv: https://arxiv.org/abs/2401.06772 · HTML: https://mlsanity.com/paper/2401.06772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [Question Answering with Subgraph Embeddings](https://mlsanity.com/paper/1406.3676/) (2014)
- [Learning to Paraphrase for Question Answering](https://mlsanity.com/paper/1708.06022/) (2017)
- [Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks](https://mlsanity.com/paper/1804.00823/) (2018)
- [Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing](https://mlsanity.com/paper/1809.00773/) (2018)
- [Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1811.01118/) (2018)
- [S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking](https://mlsanity.com/paper/1609.08075/) (2016)
- [Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1907.09361/) (2019)
- [Learning an Executable Neural Semantic Parser](https://mlsanity.com/paper/1711.05066/) (2017)
- [CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases](https://mlsanity.com/paper/1606.01994/) (2016)
- [Template-Based Question Answering over Linked Geospatial Data](https://mlsanity.com/paper/2007.07060/) (2020)
- [Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information](https://mlsanity.com/paper/1606.00979/) (2016)
- [Sentence Rewriting for Semantic Parsing](https://mlsanity.com/paper/1901.02998/) (2019)

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

- [SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2512.04868/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel method for question answering over knowledge graphs based on graph-to-segment mapping, designed to improve the understanding of natural language questions. Our approach is grounded in semantic parsing, a key technique for interpreting question utterances. The main challenges arise from handling implicit entities and relations, as well as complex constraints such as temporal conditions, ordinality, and aggregation within the context of a knowledge graph. To address these issues, our framework integrates both rule-based and neural methods to parse and construct accurate, comprehensive semantic segment sequences. These sequences are then assembled into semantic query graphs, providing precise representations of question utterances. We formulate question semantic parsing as a sequence generation task, employing an encoder-decoder neural network to map natural language questions into semantic segments. Furthermore, to enhance the identification of implicit entities and relations, we incorporate a graph neural network that leverages knowledge graph context to enrich question representations. Experimental evaluations on two benchmark datasets demonstrate the effectiveness and superior performance of our model in semantic parsing for knowledge graph question answering.