mlsanityData to 7 Oct 2026

QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers

arXiv 2202.00120 ↗First posted 31 Jan 2022cs.CL cs.IRcited by 39

Not on the map. Why?

Abstract (arXiv)

The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual aspect of accessibility of KGQA systems, we identified several ongoing challenges. One of them is the lack of multilingual KGQA benchmarks. In this work, we extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages provided by native speakers, and transferring the SPARQL queries of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before. The latter two of the languages are considered as "endangered" by UNESCO. We call the extended dataset QALD-9-plus and made it available online https://github.com/Perevalov/qald_9_plus.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

3 of 19 references.

References · 3

  1. 2019Measuring Compositional Generalization: A Comprehensive Method on Realistic Datacited by 280
  2. 2018Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answeringcited by 30
  3. 2018Towards a Question Answering System over the Semantic Webcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 12; 2025: 15; 2026: 6.

39 papers.

Sort:

By area:

  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  2. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 235
  3. 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 36
  4. 2024SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questionscited by 27
  5. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  6. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  7. 2024An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaborationcited by 16
  8. 2024EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphscited by 14
  9. 2024Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistancecited by 13
  10. 2025Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarkingcited by 11
  11. 2022Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languagescited by 10
  12. 2025Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphscited by 9
27 more
  1. 2024Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answeringcited by 8
  2. 2024ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphscited by 8
  3. 2025Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translationcited by 8
  4. 2022Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questionscited by 7
  5. 2024Towards Automatic Composition of ASP Programs from Natural Language Specificationscited by 7
  6. 2025Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enrichingcited by 7
  7. 2024MST5 -- Multilingual Question Answering over Knowledge Graphscited by 5
  8. 2025SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injectioncited by 5
  9. 2025FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphscited by 5
  10. 2025Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphscited by 5
  11. 2024MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detectioncited by 4
  12. 2025Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoningcited by 4
  13. 2023Data Distribution Bottlenecks in Grounding Language Models to Knowledge Basescited by 3
  14. 2023GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Modelscited by 2
  15. 2025SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questionscited by 2
  16. 2024Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Constructioncited by 1
  17. 2025Protoknowledge Shapes Behaviour of LLMs in Downstream Tasks: Memorization and Generalization with Knowledge Graphscited by 1
  18. 2025Automating SPARQL Query Translations between DBpedia and Wikidatacited by 1
  19. 2025From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphscited by 1
  20. 2026PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarkingcited by 1
  21. 2026Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLPcited by 1
  22. 2022Question-type Identification for Academic Questions in Online Learning Platformcited by 0
  23. 2025Explainable Benchmarking through the Lense of Concept Learningcited by 0
  24. 2026SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generationcited by 0
  25. 2026MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQAcited by 0
  26. 2026Towards Researcher Agents for Knowledge-Graph Question Answeringcited by 0
  27. 2026Compositional Chain-of-Relations for Faithful Knowledge Graph Question Answering with Large Language Modelscited by 0