Browsing Data Science Institute by Subject "knowledge graphs"
Now showing items 1-4 of 4
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Drug target discovery using knowledge graph embeddings
(Association for Computing Machinery, 2019-04-08)The field of drug discovery has entered a plateau stage lately. It is increasingly more expensive and time-demanding to introduce new drugs into the market. One of the main reasons is the slow progress in finding novel ... -
Knowledge graph driven approach to represent video streams for spatiotemporal event pattern matching in complex event processing
(World Scientific Publishing, 2020)Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video ... -
Link prediction using multi part embeddings
(NUI Galway, 2019-06-02)Knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation ... -
Uncovering semantic bias in neural network models using a knowledge graph
(ACM, 2020-10-19)While neural networks models have shown impressive performance in many NLP tasks, lack of interpretability is often seen as a disadvantage. Individual relevance scores assigned by post-hoc explanation methods are not ...