This Vision Interview with Petr Knoth, Senior Research Fellow in Text and Data Mining at the Open University and Head of CORE (core.ac.uk), served as the opening segment of the NISO Hot Topic virtual conference, Text and Data Mining, held on May 25, 2022. Todd Carpenter spoke at length with Knoth about the many ways in which text and data mining impacts the present as well as the future. They discussed just how innovative this technology can be for the needs of researchers in the information community.
Implementing the CORE Recommender in Strathprints: a “whitehat” improvement to promote user interaction
This guest blog post briefly reviews why the CORE Recommender was quickly adopted on Strathprints and how it has become a central part of our quest to improve the interactive qualities of repositories.
Back in October 2016 my colleagues at the CORE Team released their Recommender plugin. The CORE Recommender plugin can be installed on repositories and journal systems to recommend similar scholarly content. On this very blog, Nancy Pontika, Lucas Anastasiou and Petr Knoth, announced the release of the Recommender as a:
…great opportunity to improve the functionality of repositories by unleashing the power of recommendation over a huge collection of open-access documents, currently 37 million metadata records and more than 4 million full-text, available in CORE*.
(* Note from CORE Team: the up-to-date numbers are 80,097,014 metadata and 8,586,179 full-text records.).
When the CORE Recommender is deployed a repository user will find that as they are viewing an article or abstract page within the repository, they will be presented with recommendations for other related research outputs, all mined from CORE. The Recommender sends data about the item the user is visiting to CORE. Such data include any identifiers and, where possible, accompanying metadata. The CORE response to the repository then delivers CORE’s content recommendations and a list of suggested related outputs are presented to the user in the repository user interface. The algorithm used to compute these recommendations is described in the original CORE Recommender blog post but is ultimately based on content-based filtering, citation graph analysis and analysis of the semantic relatedness between the articles in the CORE aggregation. It is therefore unlike most standard recommender engines and is an innovative application of open science in repositories.
Needless to say, we were among the first institutions to proudly implement the CORE Recommender on our EPrints repository. The implementation was on Strathprints, the University of Strathclyde’s institutional repository, and was rolled out as part of some wider work to improve repository visibility and web impact. The detail of this other work can be found in a poster presented at the 2017 Repository Fringe Conference and
a recent blog post
CORE now offers 5 millions of open access full-text research papers
“In the last year, we have managed to scale up our harvesting process. This enabled us to significantly increase the amount of open access content we can offer to our users. With more and more open access content being made available by data providers, thanks to recent open access policies, CORE now also captures and provides access to a higher percentage of global research literature ”, says CORE’s founder, Dr Petr Knoth.
With 66 million metadata records and 5 million full-text, from 102 countries, in 52 different languages, CORE becomes now the world’s largest full-text open access aggregator. CORE embraces the vibrant collections of both institutional and disciplinary repositories, while its large volume of scholarly outputs ranges from scientific research papers, to grey literature and from Master’s to Doctoral thesis. In addition, it is a metasearch for the all the open access peer-reviewed scientific journal articles published in open access journals.