Towards a methodology for the semi-automatic generation of scientific knowledge graphs from XML documents

Robots used in analytical laboratories, such as those at Unilever, generate vast amounts of log data. This log data is typically stored in semi-structured formats (e.g. XML) according to some standard schema, e.g. the Analytical Information Markup Language (AnIML). Representing this data in a structured format such as a knowledge graph would allow for a more consistent data interpretation, as the relationships between concepts would be formalised in an ontology; consequently making the process of complex data analysis simpler for the scientists involved. We propose a semi-automatic pipeline that exploits the inherent structure of XML schemata, as well as previously represented domain knowledge, to create a knowledge graph that represents log data with its relevant metadata. We utilise ontology alignment techniques to identify related concepts in different ontologies, and therefore provide additional context when predicting the property linking two classes while building the graph

Recent applications and potential of near-term (interannual to decadal) climate predictions

Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy

Autonomy in the Age of Knowledge Graphs: Vision and Challenges

In this position paper, we propose that Knowledge Graphs (KGs) are one of the prime approaches to support the programming of autonomous software systems at the knowledge level. From this viewpoint, we survey how KGs can support different dimensions of autonomy in such systems: For example, the autonomy of systems with respect to their environment, or with respect to organisations; and we discuss related practical and research challenges. We emphasise that KGs need to be able to support systems of autonomous software agents that are themselves highly heterogeneous, which limits how these systems may use KGs. Furthermore, these heterogeneous software agents may populate highly dynamic environments, which implies that they require adaptive KGs. The scale of the envisioned systems - possibly stretching to the size of the Internet - highlights the maintainability of the underlying KGs that need to contain large-scale knowledge, which requires that KGs are maintained jointly by humans and machines. Furthermore, autonomous agents require procedural knowledge, and KGs should hence be explored more towards the provisioning of such knowledge to augment autonomous behaviour. Finally, we highlight the importance of modelling choices, including with respect to the selected abstraction level when modelling and with respect to the provisioning of more expressive constraint languages

Quantity and structure of surfactant proteins vary among patients with alveolar proteinosis

I. R. Doyle, K. G. Davidson, H. A. Barr, T. E. Nicholas, K. Payne, and J. Pfitzne