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Semantometrics: Towards Fulltext-based Research Evaluation

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Over the recent years, there has been a growing interest in developing new scientometric measures that could go beyond the traditional citation-based bibliometric measures. This interest is motivated on one side by the wider availability or even emergence of new information evidencing research performance, such as article downloads, views and twitter mentions, and on the other side by the continued frustrations and problems surrounding the application of citation-based metrics to evaluate research performance in practice.
Semantometrics are a new class of research evaluation metrics which build on the premise that full text is needed to assess the value of a publication. This talk will present the results of an investigation into the properties of the semantometric contribution measure (Knoth & Herrmannova, 2014). We will provide a comparative evaluation of the contribution measure with traditional bibliometric measures based on citation counting.

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Semantometrics: Towards Fulltext-based Research Evaluation

  1. 1. 1/26 Semantometrics: Towards full text-based research evaluation Petr Knoth and Drahomira Herrmannova Semantometrics.org
  2. 2. 2/26 Towards full-text based research metrics: Exploring semantometrics 13th June 2016: Announcement of the report release: https://scholarlyfutures.jiscinvolve.or g/wp/2016/06/towards-full-text- based-research-metrics-exploring- semantometrics/ Report available at: http://repository.jisc.ac.uk/6376/1/Ji sc-semantometrics-experiments- report-final.pdf
  3. 3. 3/26 Current impact metrics • Pros: simplicity • Cons: insufficient evidence they capture quality and research contribution, ad-hoc/established axiomatically
  4. 4. 4/26 The crisis of research evaluation? Figure: Rejection rates vs Journal Impact Factor (JIF) according to (da Silva, 2015).
  5. 5. 5/26 Problems of current impact metrics • Sentiment, semantics, context and motives [Nicolaisen, 2007] • Popularity and size of research communities [Brumback, 2009; Seglen, 1997] • Time delay [Priem and Hemminger, 2010] • Skewness of the distribution [Seglen, 1992] • Differences between types of research papers [Seglen, 1997] • Ability to game/manipulate citations [Arnold and Fowler, 2010; PLoS Medicine Editors, 2006]
  6. 6. 6/26 Alternative metrics • Alt-/Webo-metrics etc. – Impact still dependent on the number of interactions in a scholarly communication network (downloads, views, readers, tweets, etc.)
  7. 7. 7/26 Semantometrics Contribution to the discipline assessed by using the article manuscript
  8. 8. 8/26 Many possibilities for semantometrics … • Detecting good research practices were followed (sound methodology, research data/code shared …) • Detecting paper type … • Analysing citation contexts (tracking facts propagation) … • Detecting the sentiment of citations … • Normalising by size of community that is likely to read the research … • Detecting good writing style …
  9. 9. 9/26 Semantometrics – contribution metric Hypothesis: Added value of publication p can be estimated based on the semantic distance from the publications cited by p to publications citing p. Detailed explanation: http://semantometrics.org
  10. 10. 10/26 Contribution metric • Based on semantic distance between citing and cited publications – Cited publications – state-of-the-art in the domain of the publication in question – Citing publications – areas of application
  11. 11. 11/26 Contribution metric – a practical example • Below- and above-average publication
  12. 12. 12/26 Experiment – dataset • Obtained by merging three open datasets: – Connecting Repositories (CORE) – OA publications, metadata and full-texts – Microsoft Academic Graph (MAG) – citation network – Mendeley – publication texts (abstracts) and readership information • Over 1.6 million CORE publications, over 12 million publications in total
  13. 13. 13/26 Experiment – dataset statistics Articles from CORE matched with MAG 1,655,835 Average number of received citations 16.09 Standard deviation 66.30 Max number of received citations 13,979 Average readership 15.94 Standard deviation 42.17 Max readership 15,193 Average contribution value 0.89 Standard deviation 0.0810 Total number of publications 12,075,238
  14. 14. 14/26 Experiment – dataset statistics • Citation and readership distribution
  15. 15. 15/26 Experiments – dataset statistics • Contribution distribution
  16. 16. 16/26 Experiment – dataset statistics • Relation between citations and readership
  17. 17. 17/26 Experiment – results • No direct correlation between contribution measure and citations/readership • When working with mean citation, readership and contribution values a clear behavioral trend emerges
  18. 18. 18/26 Experiment – results • Relation between mean contribution and citations
  19. 19. 19/26 Experiment – results • Relation between mean contribution and readership
  20. 20. 20/26 Current impact metrics vs semantometrics Unaffected by Current impact metrics Semantometrics Citation sentiment, semantics, context, motives ✗ ✔ Popularity & size of res. communities ✗ ✔ Time delay ✗ ✗/✔* Skewness of the citation distribution ✗ ✔ Differences between types of res. papers ✗ ✔ Ability to game/manipulate the metrics ✗ ✗/✔** * reduced to 1 citation ** assuming that self-citations are not taken into account 12
  21. 21. 21/26 Metrics for evaluating article sets • Encourage focusing on quality rather than quantity • Comparable regardless of discipline, seniority, etc.
  22. 22. 22/26 Evaluating research metrics • Need for a data driven approach – Ground truth – Human judgments – Many facets of performance (societal impact, economical impact, rigour, originality/novelty)
  23. 23. 23/26 WSDM Cup – work on new metrics • The goal of the challenge is to assess the query-independent importance of scholarly articles, using data from the Microsoft Academic Graph (>120M papers). • Human judgements • But no full text in MAG
  24. 24. 24/26 Dataset for semantometrics research By connecting MAG, CORE and Mendeley data, we have a dataset to study semantometrics.
  25. 25. 25/26 Conclusions • Full-text necessary for research evaluation • Semantometrics are a new class of methods. • We are studying one semantometric method to assess the research contribution • Need for a data driven approach for evaluating metrics
  26. 26. 26/26 References • Jeppe Nicolaisen. 2007. Citation Analysis. Annual Review of Information Science and Technology, 41(1):609-641. • Douglas N Arnold and Kristine K Fowler. 2010. Nefarious numbers. Notices of the American Mathematical Society, 58(3):434-437. • Roger A Brumback. 2009. Impact factor wars: Episode V -- The Empire Strikes Back. Journal of child neurology, 24(3):260-2, March. • The PLoS Medicine Editors. 2006. The impact factor game. PLoS medicine, 3(6), June.
  27. 27. 27/26 References • Jason Priem and Bradely M. Hemminger. 2010. Scientometrics 2.0: Toward new metrics of scholarly impact on the social Web. First Monday, 15(7), July. • Per Ottar Seglen. 1992. The Skewness of Science. Journal of the American Society for Information Science, 43(9):628-638, October. • Per Ottar Seglen. 1997. Why the impact factor of journals should not be used for evaluating research. BMJ: British Medical Journal, 314(February):498-502.

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