%0 Book Section %1 glauer2023neurosymbolic %A Glauer, Martin %A Mossakowski, Till %A Neuhaus, Fabian %A Memariani, Adel %A Hastings, Janna %B A Compendium of Neuro-Symbolic Artificial Intelligence %D 2023 %E Hitzler, Pascal %E Sarker, Md Kamruzzaman %E Eberhart, Aaron %I IOS press %K chemistry myown neuro-symbolic ontology %P 460 - 484 %R 10.3233/FAIA230153 %T Neuro-symbolic semantic learning for chemistry %U https://ebooks.iospress.nl/volumearticle/63730 %V 369 %X Ontologies represent human domain expertise symbolically in a way that is accessible to human experts and suitable for a variety of applications. As a result, they are widely used in scientific research. A challenge for the neuro-symbolic field is how to use the knowledge encoded in ontologies together with sub-symbolic learning approaches. In this chapter we describe a general neuro-symbolic architecture for using knowledge from ontologies to improve the generalisability and accuracy of predictions of a deep neural network applied to chemical data. The architecture consists of a multi-layer network with a multi-step training process: first, the network is given a self-supervised pre-training step with a masked language step in order to learn the input representation. Second, the network is given an ontology pre-training step in which the network learns to predict membership in the classes of the ontology as a way to learn organising knowledge from the ontology. Finally, we show that visualisation of the attention weights of the ontology-trained network allows some form of interpretability of network predictions. In general, we propose a three-layered architecture for neuro-symbolic integration, with layers for 1) encoding, 2) ontological classification, and 3) ontology-driven logical loss. %& 21

@incollection{glauer2023neurosymbolic, abstract = {Ontologies represent human domain expertise symbolically in a way that is accessible to human experts and suitable for a variety of applications. As a result, they are widely used in scientific research. A challenge for the neuro-symbolic field is how to use the knowledge encoded in ontologies together with sub-symbolic learning approaches. In this chapter we describe a general neuro-symbolic architecture for using knowledge from ontologies to improve the generalisability and accuracy of predictions of a deep neural network applied to chemical data. The architecture consists of a multi-layer network with a multi-step training process: first, the network is given a self-supervised pre-training step with a masked language step in order to learn the input representation. Second, the network is given an ontology pre-training step in which the network learns to predict membership in the classes of the ontology as a way to learn organising knowledge from the ontology. Finally, we show that visualisation of the attention weights of the ontology-trained network allows some form of interpretability of network predictions. In general, we propose a three-layered architecture for neuro-symbolic integration, with layers for 1) encoding, 2) ontological classification, and 3) ontology-driven logical loss.}, added-at = {2023-05-27T21:50:55.000+0200}, author = {Glauer, Martin and Mossakowski, Till and Neuhaus, Fabian and Memariani, Adel and Hastings, Janna}, biburl = {https://www.bibsonomy.org/bibtex/2a6e81e065a4589e72684474d485bd3ab/tillmo}, booktitle = {A Compendium of Neuro-Symbolic Artificial Intelligence}, chapter = 21, doi = {10.3233/FAIA230153}, editor = {Hitzler, Pascal and Sarker, Md Kamruzzaman and Eberhart, Aaron}, interhash = {d9b0ff9f2fcb54585be85987f1186a9b}, intrahash = {a6e81e065a4589e72684474d485bd3ab}, keywords = {chemistry myown neuro-symbolic ontology}, pages = { 460 - 484}, publisher = {IOS press}, series = {Frontiers in Artificial Intelligence and Applications}, timestamp = {2023-08-04T18:19:04.000+0200}, title = {Neuro-symbolic semantic learning for chemistry}, url = {https://ebooks.iospress.nl/volumearticle/63730}, volume = 369, year = 2023 }