mlsanityData to 7 Oct 2026

Semantic Parsing for Conversational Question Answering over Knowledge Graphs

arXiv 2301.12217 ↗First posted 28 Jan 2023cs.CLcited by 10

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Abstract (arXiv)

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 28 Jan 2023.

Builds on

27 of 43 references.

References · 27

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15 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 3; 2026: 1.

10 papers.

  1. 2024Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Factscited by 14
  2. 2023Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generationcited by 7
  3. 2024Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphscited by 7
  4. 2024Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answeringcited by 3
  5. 2025Towards Adaptive Context Management for Intelligent Conversational Question Answeringcited by 3
  6. 2023Conversational Semantic Parsing using Dynamic Context Graphscited by 2
  7. 2023KGConv, a Conversational Corpus grounded in Wikidatacited by 1
  8. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  9. 2025SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphscited by 1
  10. 2026Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understandingcited by 0