Knowledge graph embedding for ecotoxicological effect prediction

Exploring the effects of a chemical compound on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. Here, we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical knowledge. These knowledge sources are integrated by ontology alignment techniques. Our experimental results show that the knowledge graph and its embeddings augment the baseline models.publishedVersio

Modelling the future of the arctic sea ice cover

Record lows in sea ice cover have recently sparked new interest in the small ice cap instability. The change in albedo when sea ice becomes open water introduces a nonlinearity called the ice-albedo feedback. Forcing a joint energy- balance and sea ice model can lead to unstable ice caps in certain parameter regimes. When the ice caps are unstable, a small perturbation will initiate a tipping point in the sea ice cover. For tipping points in general, a number of studies have pointed out that increasing variance and autocorrelation in time series can be used to predict abrupt transitions, but that the rise in one alone, can cause false alarms. In this study, we will examine these methods, as well as propose new methods that are specific to the problem at hand, and that are more robust when it comes to predicting the abrupt change in sea ice cover. We further investigate the hysteresis that occurs after an abrupt transition and show that the thermal inertia of the deep ocean may delay the recovery of the sea ice cover by several decades in scenarios where pre-industrial CO2 concentration is restored on century time scale

Knowledge graph embedding for ecotoxicological effect prediction

Exploring the effects of a chemical compound on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. Here, we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical knowledge. These knowledge sources are integrated by ontology alignment techniques. Our experimental results show that the knowledge graph and its embeddings augment the baseline models