Towards Quality-of-Service Metrics for Symbolic Knowledge Injection

Andrea Rafanelli

The integration of symbolic knowledge and sub-symbolic predictors represents a recent popular trend in AI. Among the set of integration approaches, Symbolic Knowledge Injection (SKI) proposes the exploitation of human-intelligible knowledge to steer sub-symbolic models towards some desired  behaviour. The vast majority of works in the field of SKI aim at increasing the predictive performance of the sub-symbolic model at hand and, therefore, measure SKI strength solely based on performance improvements. However, a variety of artefacts exist that affect this measure, mostly linked to the quality of the injected knowledge and the underlying predictor. Moreover, the use of injection techniques introduces the possibility of producing more efficient sub-symbolic models in terms of computations, energy, and data required. Therefore, novel and reliable Quality-of-Service (QoS) measures for SKI are clearly needed, aiming at robustly identifying the overall quality of an injection mechanism. Accordingly, in this work, we propose and mathematically model the first – up to our knowledge – set of QoS metrics for SKI, focusing on measuring injection robustness and efficiency gain.


  • 23rd Workshop “From Objects to Agents” (WOA 2022) — 01/09/2022–03/09/2022


Talks / Views


•  tags  •  speakers  •  authors  

 2023    2022    2021    2020    2019    2018    2017    2016    2015    2014–1992

•  talks  •  invited  •  seminars  •  lectures  •  tutorials  •  project  •  informal  •  internal  •  panel  •  PhD  •  poster  •  others  


— speakers

— authors

— sort


— language


— context

WOA 2022

— where

Genova, Italy

— when


Partita IVA: 01131710376 — Copyright © 2008–2023 APICe@DISI – PRIVACY