Building a Massive Biomedical Knowledge Graph with Citizen Science

The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism... [ view full abstract ]

The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism involving billions of nucleotides in a few days to imaging systems that can just as rapidly churn out millions of snapshots of cells, biology is truly faced with a data deluge. To translate this information into new knowledge that can guide the search for new medicines, biomedical researchers increasingly need to build on the existing knowledge of the broad community. Prior knowledge can help guide searches through the masses of new data. Unfortunately, most biomedical knowledge is represented solely in the text of journal articles. Given that more than a million such articles are published every year, the challenge of using this knowledge effectively is substantial. Ideally, knowledge such as the interrelations between genes, drugs and diseases would be represented in a knowledge graph that enabled queries like: “show me all the genes related to this disease or related to any drugs used to treat this disease.” Systems exist that attempt to extract this information automatically from text, but the quality of their output remains far below what can be obtained by human readers. We are developing a new platform that taps the language comprehension abilities of citizen scientists to help excavate a query-able knowledge graph from the biomedical literature. In proof-of-concept experiments, we have demonstrated that lay-people are capable of extracting meaningful information from complex biological text. The information extracted using this community intelligence framework can surpass the efforts of individual experts in quality while also offering the potential to achieve massive scale. In this presentation we will describe the results of early experiments and introduce our prototype citizen science platform: http://mark2cure.org.