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

Source: [PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding](<https://www.sota2.com/research/paper/68fa70e71e209131cea4ddb3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:52:52.049Z

Authors: Hanna Abi Akl

Published: 2023-10-19T10:53:06.000Z

## Abstract

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%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.12638>)

[PDF](<https://arxiv.org/pdf/2310.12638.pdf>)