Abstract
The social media revolution is having a dramatic effect on the world of scientific publication. Scientists now publish their research interests, theories and outcomes across numerous channels, including personal blogs and other thematic web spaces where ideas, activities and partial results are discussed. Accordingly, information systems that facilitate access to scientific literature must learn to cope with this valuable and varied data, evolving to make this research easily discoverable and available to end users. In this paper we describe the incremental process of discovering web resources in the domain of agricultural science and technology. Making use of Linked Open Data methodologies, we interlink a wide array of custom-crawled resources with the AGRIS bibliographic database in order to enrich the user experience of the AGRIS website. We also discuss the SemaGrow Stack, a query federation and data integration infrastructure used to estimate the semantic distance between crawled web resources and AGRIS.
Highlights
AGRIS is the International System for Agricultural Science and Technology, a collection of nearly 8 million multilingual bibliographic resources spanning the last forty years and produced by a network of more than 150 institutions from 65 countries
Since December 2013, AGRIS adopted a LOD (Linked Open Data) infrastructure [Anibaldi et al, 2015], which allowed the creation of mashup pages, where users looking for specific topics can access a publication from the AGRIS database, combined with other related resources extracted from other preselected datasets
This paper describes a proposed solution to discover such knowledge making use of modified open source software (Nutch and Maui) together with the SemaGrow Stack and a custom recommender in order to enrich the relevance of AGRIS bibliographic resources and the AGRIS web portal mashup
Summary
Any reports and responses or comments on the article can be found at the end of the article. We conducted an evaluation study on a benchmark sample of AGRIS articles in order to determine the relevance between crawled web resources and the AGRIS database. We computed the precision of recommendations considered as “relevant” by our algorithm, commenting on some possible improvements to the process used and described in our work. Outcomes of our evaluation study are presented in the “Analysis of relevance” section, together with a new picture displaying the cumulative distribution of AGRIS records over the number of relevant recommendations. The section “The output of the recommender system” was removed, since it contained only a sample RDF/XML fragment that was not very significant. The definition of the custom algorithm was removed and minor improvements have been made to the text, as suggested by reviewers
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