| Challenge: | Knowledge Graphs (KGs) are becoming increasingly popular as a means of storing structured data. |
| Approach: | They propose a method to generate training data for semantic parsing over Property Graphs without human annotations by matching tree patterns to the KG and paraphrasing the query program with an LLM. |
| Outcome: | The proposed method generates training data for parsing over Property Graphs without human annotations on two property graph benchmarks utilizing the Cypher query language. |
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On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens. |
| Approach: | They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability. |
| Outcome: | The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations. |
ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs (2025.findings-emnlp)
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| Challenge: | PGs are increasingly used in knowledge graphs, but they are underrepresented in research . a benchmark is designed specifically for PG and queries written in Cypher. |
| Approach: | They propose a benchmark specifically for PGs and queries written in Cypher. |
| Outcome: | The proposed benchmark is designed specifically for PGs and queries written in Cypher. |
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)
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| Challenge: | Existing models for KG-to-text generation are based on pretrained language models. |
| Approach: | They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. |
| Outcome: | The proposed model outperforms all comparison methods on fully-supervised and fewshot settings. |
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
| Approach: | They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems. |
| Outcome: | The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems. |
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)
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| Challenge: | Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages. |
| Approach: | They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. |
| Outcome: | The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages. |
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)
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| Challenge: | Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain. |
| Approach: | They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference . |
| Outcome: | The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples. |
Zero-Shot Semantic Parsing for Instructions (P19-1)
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| Challenge: | Recent years have seen an increasing number of applications that have a natural language interface, such as chatbots or "intelligent personal assistants" |
| Approach: | They propose a new training algorithm that trains a semantic parser on examples from a set of source domains and augment it with features and a logical form candidate filtering logic to support zero-shot adaptation. |
| Outcome: | The proposed framework performs better than a non-adapted parser with features and logical form candidate filtering logic. |
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)
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| Challenge: | Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data. |
| Approach: | They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
| Outcome: | The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (N18-1)
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| Challenge: | Existing factoid question answering systems rely on annotated datasets such as SimpleQuestions to generate questions from knowledge graphs. |
| Approach: | They propose a neural model that generates questions from knowledge graphs triples in a “zero-shot” setup. |
| Outcome: | The proposed model outperforms state-of-the-art on this task. |