PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding
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Abstract (arXiv)
The Scholarly Question Answering over Linked Data (Scholarly QALD) at The International Semantic Web Conference (ISWC) 2023 challenge presents two sub-tasks to tackle question answering (QA) over knowledge graphs (KGs). We answer the KGQA over DBLP (DBLP-QUAD) task by proposing a neuro-symbolic (NS) framework based on PSYCHIC, an extractive QA model capable of identifying the query and entities related to a KG question. Our system achieved a F1 score of 00.18% on question answering and came in third place for entity linking (EL) with a score of 71.00%.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 19 Oct 2023.
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10 of 28 references.
References · 10
- 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
- 2023Knowledge Graphs: Opportunities and Challengescited by 70
- 2023DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graphcited by 18
- 2021UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Textcited by 17
- 2022ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphscited by 16
- 2019Question Answering over Knowledge Graphs via Structural Query Patternscited by 6
- 2022Improving Question Answering over Knowledge Graphs Using Graph Summarizationcited by 6
- 2020PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphscited by 5
- 2019Advances in Natural Language Question Answering: A Reviewcited by 4
- 2021Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linkingcited by 3
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.