Data in the life sciences is vast, ever expanding, and captured in a plethora of different formats. To extract actionable insights, that may otherwise remain unseen, from such a complex data landscape, sources need to be harmonised, or integrated. Knowledge graphs provide an intuitive means of representing these connected data and utilise ontologies to encode the semantics, or meaning, of entities and the relationships that exist between them.
One example where such an approach may prove beneficial is drug repositioning. By focussing on pre-approved drugs, with existing clinical safety profiles, drug repositioning has the potential to reduce both the time and cost of getting a drug to market. Typically, such findings have been made serendipitously, but by pulling together relevant data into a knowledge graph, a more systematic approach may be taken.
In this webinar we present the development of a knowledge graph in the area of drug repositioning. We show how SciBite and Stardog technology enable pre-existing unstructured and semi-structured data to be combined into a rich knowledge graph. It is shown how the SciBite semantic platform: supports the creation of knowledge graph schema based on relevant ontologies; aligns unstructured data to these ontologies; identifies-occurrence relations between entities. Finally, we demonstrate how this data may be loaded into Stardog, integrated with other sources and subsequently queried.
In this blog we describe the pivotal role of semantic enrichment in the creation of effective Knowledge Graphs, and illustrate how semantic Knowledge Graphs help answer complex scientific questions.Read
Public ontologies are essential for applying FAIR principles to data but are not built for use in named entity recognition pipelines. At SciBite, we build on the public ontologies to create VOCabs optimized for NER. In this blog, discover how we create a SciBite VOCab from a Public Ontology.Read
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