AI3SD Video: Automated chemical ontology expansion
AI3SD Video: Automated chemical ontology expansion
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.
AI, AI3SD Event, Artificial Intelligence, Data Science, Ontologies, OWL, RDF, Research, Research Data Managementq, Responsible Research, Semantic Web, Semantics, SPARQL
Hastings, Janna
8aa73d04-52db-4017-8ff7-5b67b1c9a8d7
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
13 October 2021
Hastings, Janna
8aa73d04-52db-4017-8ff7-5b67b1c9a8d7
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hastings, Janna
(2021)
AI3SD Video: Automated chemical ontology expansion.
Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan
(eds.)
AI3SD Autumn Seminar Series 2021.
13 Oct - 15 Dec 2021.
(doi:10.5258/SOTON/AI3SD0154).
Record type:
Conference or Workshop Item
(Other)
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.
Video
AI3SDAutumnSeminar-131021-JannaHastings
- Version of Record
More information
Published date: 13 October 2021
Additional Information:
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.
Venue - Dates:
AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15
Keywords:
AI, AI3SD Event, Artificial Intelligence, Data Science, Ontologies, OWL, RDF, Research, Research Data Managementq, Responsible Research, Semantic Web, Semantics, SPARQL
Identifiers
Local EPrints ID: 451925
URI: http://eprints.soton.ac.uk/id/eprint/451925
PURE UUID: 4e298d93-81f0-479b-bdc8-03b9ac195031
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Date deposited: 03 Nov 2021 17:45
Last modified: 17 Mar 2024 03:51
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Contributors
Author:
Janna Hastings
Editor:
Mahesan Niranjan
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