Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024

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Bringing Back Semantics to Knowledge Graph Embeddings: An Interpretability Approachach to derive interpretable vector spaces with human-understandable dimensions in terms of the features of the entities. We demonstrate the efficacy of . in encapsulating desired semantic features, presenting evaluations both in the vector space as well as in terms of semantic similarity measurements.

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Conference proceedings 2024resented the latest and ongoing research work on neurosymbolic AI. Neurosymbolic AI aims to build rich computational models and systems by combining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their.complementary weaknesses..

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0302-9743 eld in Barcelona, Spain during September 9-12th, 2024...The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI. Neurosymbolic AI aims to build rich computational models and systems by c

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Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction 9 out of 14 molecular property prediction datasets. This highlights the importance of utilizing a larger and more diverse set of functional groups to enhance molecular representations for property predictions.

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978-3-031-71169-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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