Springer Nature advances discovery by publishing robust and insightful research, supporting the development of new areas of knowledge, making ideas and information accessible around the world, and leading the way on open access. The company is also a leading educational and professional publisher, providing quality content through a range of innovative platforms, products and services.
Springer Nature (SN) wanted to create a platform where to house all of their publications dating back as far as 1815 and currently amounting to 13 million. With about 500 000 documents published every year (articles, chapters, journals, books, etc.), this figure was expected to increase significantly in the future.
However, the company’s long-term goal was not to create just a readily-available repository of the content they produced and maintained but instead to develop a new platform for scientists. This platform needed to be richly interlinked both at the user and the content level and to be powered by an industry-specific knowledge graph, representing the concepts that Springer Nature’s readers cared about.
One of the biggest difficulties In research communities, as in a lot of enterprises, is that there are many islands of content where knowledge is locked. The challenge is to make all the pieces of that knowledge talk to each other to allow better discoverability of information and ultimately its optimal use.
Some of the main problems coming from scattered and disconnected content are:
At a high level of abstraction, there are three areas of knowledge that scientists are most concerned about: the science they are interested in, the documents they read and write, and the people who carry out scientific research. This simplified picture can be further broadened with, for example, the institutions people work for, the organizations funding their research, the conferences they attend, the research group they belong to, and many more.
All this knowledge is best modeled in a knowledge graph, which generally represents real-world objects.
As illustrated in the example above, the knowledge graph enables users to easily follow the relationships researchers have with everything they do in a way that closely resembles human thinking.
SN SciGraph (projected to contain 1.5 to 2 billion triples) makes use of such a knowledge graph database, GraphDB, and its ability to handle massive load, querying and inferencing in real-time.
By seamlessly integrating disparate silos of content, GraphDB allows Springer Nature’s LOD platform to comprise metadata from journals and articles, books and chapters, organizations, institutions, funders, research grants, patents, clinical trials, substances, conference series, events, citations and reference networks, Altmetrics, and links to research datasets.
Ontotext’s technology helps SN SciGraph to collate high-quality content from trusted and reliable sources across the research landscape. This high-quality data provides a rich semantic description of how information is related and visualizes the scholarly domain in interesting new ways.
Springer Nature has a longstanding commitment to making science more accessible as well as facilitating scientists to work together. Thanks to SN SciGraph, the company now belongs to the vanguard of LOD providers and has assumed a leading role among open data publishers and open research supporters.
Do you think this case resembles your particular needs?