Knowledge Graphs for Open Science

Modelling the relationships within scientific data in an open and machine-understandable format leads to better science.

May 20, 2021 5 mins. read Jarred McGinnis

The Open Science (or Open Scholarship) movement has been gaining momentum, especially since the European Commission has committed itself to ensuring open access to all funded research in April 2016. Expensive paywalls used by publishers, restrictive usage policies by scientific literature sources, a lack of consistency in the formatting and data locked away by proprietary software have all had detrimental effects on the dissemination of scientific knowledge.

In addition, a number of other arguments have put pressure on governments to provide open access to publicly funded research, because the source of the funds were taxpayers. Others feel transparency ultimately leads to more rigorous peer review and opportunities to address truly complex problems by facilitating ‘cross-fertilisation’ of research, currently siloed or locked away by publishers. Publishers are also aware of the changes needed in their business models and have begun to modify their priorities to better align with the Open Science paradigm.

This lack of transparency has also made the crucial task of measuring scientific impact extremely difficult despite it being important for the improvement of the ‘State of the Art’ and for more accurately evaluating an individual researcher’s impact in their field and more efficient allocation of funding for promising research. Ontotext’s knowledge graph-based technologies are seen as a key to realizing the benefits of Open Science.

Tracking of Research Results

Ontotext is one of the main partners for a pilot project, Tracking of Research Results (TRR), commissioned by the European Commission to design methodologies and develop new means for tracking research results even after the contractual period of the EU-funded projects has ended. TRR will look at projects funded under Horizon 2020, FP6 and FP7 Cooperation Specific Programme, covering approximately 8000 projects from 2007 to 2012 with the goal of tracking their performance according to fourteen indicators including output, outcome and impact indicators such as publications, new IP, developed products, filed inventions and patents as well as the number of newly established startups, enterprises or ‘research networks’ created.

The Open Science movement has created an ecosystem of research results including Google Scholar, Microsoft Academic Graph, ORCID, Wikidata Scholia. Knowledge graphs are particularly good at normalising and integrating disparate datasets. The resulting knowledge graph provides a far more robust view of the research and researchers funded by the European Commission with the ability to track a project beyond the immediate impact during the life of the project itself.

Just the Beginning

The same technology is being adopted by individual research institutions. For example, research units in Ann & Robert H. Lurie Children’s Hospital of Chicago are using Ontotext to create tools to efficiently measure the impact of the activities of their faculty members. Calculating impact includes traditional factors like publications and citations in esteemed scientific research platforms like PubMed and ClinicalTrials.gov, but also includes mentions in social media and actual implementation in clinical practice. This required an efficient methodology for the integration of data from internal and external sources, which is exactly why knowledge graphs were the preferred solution.

The resulting analytical platform can use these faculty impact metrics to inform research priorities and policy focus by understanding the institute’s impact on the wider academic community and identify what institutions are most likely to fund which research topics.

Additionally, the intelligence tool can be used to identify gaps in skills or a lack of expertise to inform hiring and promotion decisions as well as automate the updating of faculty pages with their current research interests to attract the best and most appropriate candidates.

Publishers Are Changing Too

Traditional publishers have often been seen as antithetical or even antagonistic to the Open Science movement. However, there are numerous examples of publishers understanding that a shift in the business models was inevitable and have been agile in integrating ‘Open Science’ principles with new products and services. Driven by Ontotext GraphDB, publishers have been able to provide linked and open data platform that integrates billions of data points from journals and articles, books and chapters, organizations, institutions, funders, research grants, patents, clinical trials, substances, conference series, events, citations and reference networks, altmetrics as well as integrating reliable third party sources to give researchers a clear view of the research landscape of their scholarly domain.

Open Science is Good Science

Academia, when it is at its best, understands that good science requires exhaustive data. More rigour and analysis leads to better science. Unrestricted access to the current literature leads to less wasted effort duplicating work that has already been proven to be a dead end. To achieve these lofty goals the technology must be able to scale with the amount of data as well as its diversity. Knowledge graphs by abstracting data away from the low-level implementation details has proven over and over to be the solution that Open Science relies on.

Want to model the relationships within your scientific data in an open and machine-understandable format?

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Technical Author at Freelancer

Jarred McGinnis is a managing consultant in Semantic Technologies. Previously he was the Head of Research, Semantic Technologies, at the Press Association, investigating the role of technologies such as natural language processing and Linked Data in the news industry. Dr. McGinnis received his PhD in Informatics from the University of Edinburgh in 2006.

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