Conferences and other events about knowledge graphs, linked data and related topics

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AI3SD Autumn Seminar Series I: Linked Data, Ontologies & Deep Learning

October 13, 2021 @ 2:00 pm - 3:45 pm BST

AI3SD Autumn Seminar Series I: Linked Data, Ontologies & Deep Learning

“About this event”

“This seminar forms part of the AI3SD Online Seminar Series that will run across the autumn (from October 2021 to December 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Linked Data, Ontologies and Deep Learning.”

“Automated Chemical Ontology Expansion using Deep Learning – 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.”

“Towards Biological Plausibility Using Linked Open Data – Professor Egon Willighagen”

“Abstract: Behind risk assessment is experimental evidence. Behind biological knowledge is primary literature. However, because the amount of knowledge keeps growing, our experimental technologies are advancing and getting increasingly complex, even experts can no longer keep up with the progress in mechanistic understanding, outside their increasingly specialistic domain. At the same time, the number of biological questions with a simple answer keeps dropping and many modern questions have complex answers. Access to the right facts at the right time needs a change of thinking. The idea of linking facts and data at a large scale was envisioned long ago, but only recently became viable, with the introduction of the semantic web and linked open data. These new technologies make it possible to easily link remote knowledge, taking advantage of globally unique identifiers and exact meaning with ontologies [1,2]. This presentation outlines how we applied these ideas to the life sciences in general and with applications to toxicology. Using eNanoMapper [3], WikiPathways [4], and Wikidata [5], it will show how semantic web approaches can be used to answer questions that are much harder to answer with older approaches. Examples will show 1. how we can use SPARQL to return all assay experiments for all types of metal oxides, 2. how biological pathway knowledge can be combined with knowledge from chemical databases, and 3. how we can find research about and scholars that study particular genes, proteins, or toxicants.”

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October 13, 2021
2:00 pm - 3:45 pm BST
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Virtual (online event)


AI3SD Network
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