Adapting Neural Link Predictors for Data-Efficient Complex Query Answering

Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, Isabelle Augenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD a parameter-efficient score textbackslashemphadaptation model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component -- which only increases the number of model parameters by -- is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD produces significantly more accurate results than current state-of-the-art methods, improving from to Mean Reciprocal Rank values averaged across all datasets and query types while using of the available training query types. We further show that CQD is data-efficient, achieving competitive results with only of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.
Original languageEnglish
Title of host publicationArXiv
Place of PublicationNew Orleans
PublisherArXiv.org
DOIs
StatePublished - Jul 1 2023
Externally publishedYes

Keywords

  • Computer Science - Artificial Intelligence
  • Computer Science - Logic in Computer Science
  • Computer Science - Machine Learning
  • Computer Science - Neural and Evolutionary Computing

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  • DL: ICAI Discovery Lab

    van Harmelen, F., De Rijke, M., Siebert, M., Hoekstra, R., Tsatsaronis, G., Groth, P., Cochez, M., Pernisch, R., Alivanistos, D., Mansoury, M., van Hoof, H., Pal, V., Pijnenburg, T., Mitra, P., Bey, T. & de Waard, A.

    10/1/1903/31/25

    Project: Research

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