research-article

Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?

Authors:

Longquan Jiang,

Ricardo UsbeckAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3209 - 3218

https://doi.org/10.1145/3477495.3531751

Published: 07 July 2022 Publication History

Abstract

Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability. That is often due to the standard i.i.d. assumption on the underlying dataset. Recently, three levels of generalization for KGQA were defined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQA datasets for 5 different Knowledge Graphs (KGs). We show that according to this definition many existing and online available KGQA datasets are either not suited to train a generalizable KGQA system or that the datasets are based on discontinued and out-dated KGs. Generating new datasets is a costly process and, thus, is not an alternative to smaller research groups and companies. In this work, we propose a mitigation method for re-splitting available KGQA datasets to enable their applicability to evaluate generalization, without any cost and manual effort. We test our hypothesis on three KGQA datasets, i.e., LC-QuAD, LC-QuAD 2.0 and QALD-9). Experiments on re-splitted KGQA datasets demonstrate its effectiveness towards generalizability. The code and a unified way to access 18 available datasets is online at https://github.com/semantic-systems/KGQA-datasets as well as https://github.com/semantic-systems/KGQA-datasets-generalization.

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Index Terms

Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Retrieval effectiveness
    2. Retrieval tasks and goals
      1. Question answering

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

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Waseda University

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https://doi.org/10.26599/BDMA.2024.9020063
Somov ONejdl WAuer SCha MMoens MNajork M(2025)The Generalization and Error Detection in LLM-based Text-to-SQL SystemsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3707416(1077-1079)Online publication date: 10-Mar-2025
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