Papers with SPARQL
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation (2022.findings-aacl)
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| Challenge: | Current architectures are unable to integrate knowledge base schema and handle questions unseen during training rendering them unusable outside the scope of topics covered in the training set. |
| Approach: | They propose to integrate a copy mechanism for neural SPARQL query generation by adding a knowledge base layer and a dynamic knowledge base vocabulary to two Seq2Seq architectures. |
| Outcome: | The proposed model outperforms existing models on state-of-the-art datasets and shows a significant increase in performance. |
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for complex question answering are limited in the search space of all possible relation paths. |
| Approach: | They propose a method that directly generates an executable SPARQL query without simplification. |
| Outcome: | The proposed method significantly outperforms the previous methods and has higher interpretability and computational efficiency than the previous ones. |
Building Literary Corpora for Computational Literary Analysis - A Prototype to Bridge the Gap between CL and DH (L18-1)
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| Challenge: | Literature analysis using corpus-based literary analysis is slow, says aaron s. e. . literary studies researchers should focus on the research practices of literary studies, he says . |
| Approach: | et al. show litText can extract text from a 20 million word corpus using SPARQL queries. |
| Outcome: | The proposed method uses a 20 million word corpus from English, German, Spanish, French and Italian texts and an example query to identify texts where animals behave like humans as it is the case in fables. |
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
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| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
| Approach: | They decompose multi-hop questions into multiple corresponding single-hop question chains and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. |
| Outcome: | The proposed models lack zero-shot multi-hop reasoning ability when trained on single-hop questions and on logical forms. |
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are susceptible to hallucinations and out-of-distribution errors when generating KG elements, such as Uniform Resource Identifiers (URIs). |
| Approach: | They propose a SPARQL query-generating framework that uses natural language placeholders and a non-parametric memory module to retrieve and resolve the correct KG URIs. |
| Outcome: | The proposed framework significantly enhances query correctness across various LLMs, datasets, and distribution shifts while achieving the near-complete suppression of URI hallucinations. |
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata (2023.emnlp-main)
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| Challenge: | Large language models can answer many questions correctly, but can also hallucinate and give wrong answers. |
| Approach: | They propose a question-answering benchmark for Wikidata that uses SPARQL to ground large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art for QALD-7 by 3.6% in F1 score. |
Graph Explorer: Training Faithful KG Agents with Visibility-Grounded Supervision (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are strong reasoners but still hallucinate and make unreliable decisions on knowledge-intensive questions. |
| Approach: | They propose a pipeline that turns LLM into executable tool supervision without manual trace labeling. |
| Outcome: | The proposed model improves over a reproduced prompting baseline by +22.5/+16.2 points . it is based on a Graph Explorer pipeline that turns SPARQL into executable tool supervision without manual trace labeling. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| Challenge: | Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale . |
| Approach: | They propose a compositional programming language to represent the reasoning process of complex questions. |
| Outcome: | The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model . |
SymKGQA: Few-Shot Knowledge Graph Question Answering via Symbolic Program Generation and Execution (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models have led to low-level LFs that are limited to the knowledge of underlying LLM about the LF. |
| Approach: | They propose a framework that generates a symbolic LF in a few-shot setting using Large Language Models. |
| Outcome: | The proposed framework outperforms all other few-shot and many fully-supervised KGQA approaches. |
A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025.findings-emnlp)
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| Challenge: | Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) KGQA methods based on LLMs are limited in their ability to model KG structure without additional data. |
| Approach: | They propose a KGQA framework that can operate in a zero-shot setting . they propose NS-KGQA to use neural KG embeddings to model KG structure . |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot baselines by 26%. |
MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)
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| Challenge: | Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning. |
| Approach: | They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
| Outcome: | The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain (2022.lrec-1)
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Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
| Challenge: | Existing Question Answering systems for commercial aviation use a large number of documents . a Knowledge Graph (KG) guided Deep Learning (DL) based system can be used to query the documents based on accident reports . |
| Approach: | They propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering system to cater to these requirements. |
| Outcome: | The proposed system achieves 7% and 40% increase in accuracy over existing systems. |
The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)
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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
| Challenge: | Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement. |
| Approach: | They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer . |
| Outcome: | The dataset is bundled with the Decomp toolkit (v0.1) both datasets are publicly available at http://decomp.io. |
SPARQLing Database Queries from Intermediate Question Decompositions (2021.emnlp-main)
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| Challenge: | Using annotated datasets is difficult as it requires query-language expertise. |
| Approach: | They propose a crowdsourcing pipeline to annotate natural language questions using intermediate question representations. |
| Outcome: | The proposed pipeline reduces the burden of annotating a large dataset with queries by using intermediate question representations. |
Analyzing Middle High German Syntax with RDF and SPARQL (L18-1)
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| Challenge: | Using CoNLL-RDF and SPARQL Update, we analyze the diachronic changes of Middle High German syntax. |
| Approach: | They propose a rule-based shallow parser and an enrichment pipeline grounded in CoNLL-RDF and SPARQL Update for parsing. |
| Outcome: | The proposed pipeline is based on CoNLL-RDF and SPARQL Update for syntactic annotation and semantic enrichment of Middle High German. |
Adaptive Text2GQL: Integrating Structural Twig Linking and Evolutionary In-Context Learning (2026.acl-long)
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| Challenge: | Existing approaches struggle with structural hallucinations and lack adaptability in cold-start scenarios. |
| Approach: | They propose a unified, training-free framework for translating natural language into Graph Query Languages. |
| Outcome: | The proposed framework improves accuracy and executability over baselines in Graph2GQLs. |
Fintan - Flexible, Integrated Transformation and Annotation eNgineering (2020.lrec-1)
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| Challenge: | Fintan is a platform for converting heterogeneous linguistic resources to RDF. |
| Approach: | They introduce Fintan for converting heterogeneous linguistic resources to RDF with its modular architecture, workflow management and visualization features. |
| Outcome: | The Fintan platform is designed to transform linguistic resources to graphs and graphs. |
InteracSPARQL : An Interactive System for SPARQL Query Refinement Using Natural Language Explanations (2026.findings-acl)
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| Challenge: | Existing approaches for SPARQL generation rely on one-turn models. |
| Approach: | They propose a training-free interactive refinement pipeline that acts as a plug-and-play enhancement for existing SPARQL systems. |
| Outcome: | The proposed approach improves the accuracy of base models without fine-tuning . it transforms potentially flawed queries from any source into verifiable code . |
PO-KGQA: Preference Optimization for Low-Resource Complex Knowledge Graph Question Answering (2026.findings-acl)
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| Challenge: | Existing low-resource in-context learning-based knowledge graph question answering methods rely heavily on large language models to convert natural language questions into logical forms. |
| Approach: | They propose a low-resource in-context learning-based knowledge graph question answering (KGQA) that uses large language models to convert a natural language question into its corresponding logical form. |
| Outcome: | The proposed method outperforms other methods on complex benchmarks by approximately 9% (avg). |