Towards full-text based research metrics: Exploring semantometrics

See also:
Report of Experiments (PDF)
Open Citations and Responsible Metrics (PDF) – Briefing note by Cameron Neylon, Curtin University
Cameron Neylon’s comment on the experiment

The HEFCE Metrics Tide Report has started a debate and proposed directions of travel for more responsible metrics. Our intention for what we called the ‘open citation experiment’ was to support this debate, to investigate open data sources that indicators can be based on and help to inform new ways forward.

Currently, the most widely used indicators for research assessment are derived from proprietary data sources and focused on citations of the peer reviewed literature.

In many cases this data is not transparent, community governed or auditable. This comes with a number of risks for universities and funders: it encourages costly purchase of near monopoly products, results in unaccountable / un-auditable allocation of public funds and means that benchmarks are often not seen as legitimate and therefore not useful.

Image 'measurement' by Freddy Fam (CC BY-NC-ND 2.0)

Image by Freddy Fam, CC BY-NC-ND 2.0 (creativecommons.org/licenses/by-nc-nd/2.0)

The Experiments

Petr Knoth and Dasha Herrmanova from the Open University have experimented with a new approach to research assessment metrics (semantometrics) which isn’t based on citation data alone but argues that the full text is needed to assess the value of a research article. I should also add that we found this approach very promising as it makes use of a new data source – the increasing availability of open access full text options in the publication ecosystem.

The experiment was an attempt to create the first semantometric measure based on the idea of measuring an article’s contribution to the progress of scholarly discussion. At a very simplified level you could say that the indicator looks at the subject matter of the papers citing and being cited by a given paper. If the subject matter of those citing a paper differs greatly from those that are cited, then this is considered to have a greater semantic distance. This is considered desirable, as the theory goes that this will have had a greater contribution to research as it is making a greater leap between previous and new discoveries, and hence give a higher score using this metric.

To explain what this means in more detail and how the contribution measure is calculated Petr and Dasha have developed a demonstrator page: http://semantometrics.org

The experiments report provides a correlation analysis of the contribution measure with two known metrics – citation counts and Mendeley readership – and analyses the behaviour of the contribution measure in relation to these metrics. However, rather than looking for a single new metric that could complement or replace citation counting, the aim of this experiment was to present an argument for studying this area more widely, to encourage developing new semantometric methods and to demonstrate this is already possible with openly available data.

To perform this analysis, both textual data of the research papers and citation data were needed. As no such dataset existed, the experiments have been conducted on a dataset obtained by merging data from CORE, Microsoft Academic Graph and Mendeley.

The report also looks at how article-level metrics can be extended to higher-level metrics, suggesting a new, fairer approach. While more work is needed to validate the proposed approach, the report emphasizes the need to move away from ad-hoc higher-level metrics (such as the h-index) to metrics that demonstrably fulfill certain objective criteria and show good performance on real data.

To find out more about the experiment and the results see the report of experiments:  Towards full-text based research metrics: Exploring semantometrics (PDF)

Open Citation Workshop March 2016

In a workshop at the end of March 2016, we reviewed the outcomes of the experiment with a number of sector representatives and also discussed potential next steps for research assessment metrics based on an open approach more generally.

We concluded that the argument for the usefulness of full text and openness in performing research assessment is convincing but that the work into semantometrics would need to go much further. We would need to investigate, for example, how the contribution measure compares to expert judgement. This would help us to see if the proposed indicator reflects any desired characteristics of research articles.

There were also plenty of suggestions for how to raise the profile of responsibly generated and applied indicators, how to improve and build on existing open data sources for research assessment metrics and how to make new ones available.  We’re working on a more detailed plan for next steps and will share this in due course.

In the meanwhile, we invited Cameron Neylon to comment on the experiment which you can read on his blog post – Taking Responsibility: How an answer to research assessment might just be “42”.

OR2016, 13-16 June, Dublin

Petr Knoth and Dasha Herrmannova presented at the OR2016 Conference on open research evaluation metrics:

Oxford vs Cambridge Contest: Collecting Open Research Evaluation Metrics for University Ranking

We are also very interested in your views about the experiment and any thoughts you may have about open metrics and indicators for research, so please do comment below.