- Improved discoverability of publications by overcoming content silos
- Enhanced user experience by revealing semantic relations visually
- Cut down on time and resources by encouraging developers to reuse SN’s datasets
The Goal
Springer Nature (SN) wanted to create a platform to house all their publications dating back as far as 1815, currently amounting to 13 million. With about 500,000 documents published every year (articles, chapters, journals, books, and more), 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 to develop a new collaborative platform for scientists. This platform needed to be richly interlinked both at the user and the content level and to be powered by a knowledge graph, representing the concepts that Springer Nature’s readers cared about.
The Challenge
One of the greatest difficulties In research communities, as in many enterprises, is that there are multiple islands of content where knowledge is stranded. It’s difficult to make all the pieces of that knowledge talk to each other to allow better discoverability of information and optimal use. Scattered and disconnected content usually results in:
- Content integration issues
- No opportunity for content analytics
- Redundancies in production workflows
- Lack of discoverability
- Underutilized archival material
The Solution: An LOD Platform Powered by Ontotext GraphDB
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 groups 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 graph database, Ontotext 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.
Business Benefits
- Overcoming internal and external content silos in research communities
- Broadening the user perspective by semantic relations being revealed visually
- Encouraging developers to reuse Springer Nature’s datasets
- Easily accessing high-quality content from trusted and reliable sources
- Finding optimal content for analysis and recommendation tools for funders, librarians, conference organizers, etc.
- Increasing the discoverability of publications due to large parts of the datasets being freely accessible (CC BY-NC 4.0 license)
Why Choose Ontotext?
Springer Nature has a longstanding commitment to making science more accessible and facilitating scientific collaboration. 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.
