AI3SD Autumn Seminar I: Linked Data, Ontologies & Deep Learning: Talk 1 - Dr Janna Hastings
Channel: AI 4 Scientific Discovery
Date: 15/10/2021

This video forms part of the AI3SD Autumn Seminar Series 20/2021. 

This video is the first talk in the first seminar of the series: Linked Data, Ontologies & Deep Learning. 

Automated Chemical Ontology Expansion - Dr Janna Hastings

Abstract: Ontologies provide a shared vocabulary and semantic resource for a domain. Manual construction enables them to achieve high quality and capture subtle semantic nuances, essential for wide acceptance and applicability across a community. However, the manual curation process does not scale for large domains. I will present a methodology for automatic ontology extension based on deep learning using ontology annotations, and show how we apply this methodology to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We used a Transformer-based deep learning architecture trained on the chemical structures from ontology leaf nodes, and the system learns to predict membership in multiple mid-level ontology classes as a multi-class classification task. Additionally, I will illustrate how visualizing the model’s attention weights can help to explain the results by providing insight into how the model made its decisions.

Bio: I am a computer scientist interested in developing artificial intelligence-based computational systems to support research across the biological and social sciences. I am particularly interested in the interface between data science, i.e. algorithms for deriving inferences and predictions based on structured and unstructured data, and knowledge science, i.e. research that amasses, integrates and harnesses what we already know and channels that back towards efforts to make novel discoveries, towards a genuinely cumulative discovery frontier. To this end I have actively contributed to research in computational knowledge representation and reasoning, to community-wide knowledge integration via building semantic standards, and to scientific discovery research using computational approaches across a range of domains.

Further details from this series can be found at: https://www.ai3sd.org/autumn-seminar-series-2021/

Q&A available here: https://www.ai3sd.org/2021/10/27/13-10-2021-ai3sd-autumn-seminar-i-linked-data-ontologies-deep-learning/

DOI Link: http://dx.doi.org/10.5258/SOTON/AI3SD0154 

This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1