arxiv:2310.12638

PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding

Published on Oct 19, 2023

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Abstract

A neuro-symbolic framework based on PSYCHIC achieved a high F1 score in Knowledge Graph QA over the DBLP dataset and third place in entity linking.

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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%.

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