Towards Quality-of-Service Metrics for Symbolic Knowledge Injection

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Angelo Ferrando, Viviana Mascardi (a cura di)
CEUR Workshop Proceedings (AIxIA Series) 3261
Sun SITE Central Europe, RWTH Aachen University
novembre 2022

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.

parole chiavesymbolic knowledge injection, quality of service, efficiency, understandability, robustness
presentazione di riferimento
page_white_powerpointTowards Quality-of-Service Metrics for Symbolic Knowledge Injection (WOA 2022, 01/09/2022) — Andrea Rafanelli (Andrea Agiollo, Andrea Rafanelli, Andrea Omicini)
evento origine
rivista o collana
book CEUR Workshop Proceedings (CEUR-WS.org)
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pubblicazione di riferimento per presentazione
page_white_powerpointTowards Quality-of-Service Metrics for Symbolic Knowledge Injection (WOA 2022, 01/09/2022) — Andrea Rafanelli (Andrea Agiollo, Andrea Rafanelli, Andrea Omicini)