Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review

by Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, and Andrea Omicini

In this paper we focus on the issue of opacity of sub-symbolic machine-learning predictors by promoting two complementary activities—namely, symbolic knowledge extraction (SKE) and injection (SKI) from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both SKE and SKI, along with two taxonomies for the classification of SKE/SKI methods. By adopting an eXplainable AI (XAI) perspective, we highlight how such methods can be exploited to either mitigate the aforementioned opacity issue. Our taxonomies are attained by surveying and classifying existing methods from the literature, following a systematic approach, and by generalising the results of previous surveys targeting specific sub-topics of either SKE or SKI alone. More precisely, we analyse 129 methods for SKE and 117 methods for SKI, and we categorise them according to their purpose, operation, expected input/output data and predictor types. For each method, we also indicate the presence/lack of runnable software implementations. Our work may be of interest for data scientists aiming at selecting the most adequate SKE/SKI method for their needs, and also work as suggestions for researchers interested in filling the gaps of the current state of the art, as well as for developers willing to implement SKE/SKI-based technologies.

• DOI: http://dx.doi.org/10.1145/3645103

@article{skeislr-acmcs,
    acm = {3645103},
    author = {Ciatto, Giovanni and Sabbatini, Federico and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea},
    doi = {10.1145/3645103},
    journal = {ACM Computing Surveys},
    keywords = {Logic; Machine learning theory; Hybrid symbolic-numeric methods; Knowledge representation and reasoning},
    numpages = 34,
    publisher = {ACM},
    scholar = {13701373869146776438},
    semanticscholar = {267611660},
    title = {Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review},
    url = {https://dl.acm.org/doi/10.1145/3645103},
    year = 2024
}