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Discovering, Indexing and Interlinking Information Resources.

Fabrizio Celli¹, Johannes Keizer¹, Yves Jaques¹

¹Food and Agriculture Organization of the UN, Rome, Italy.

|February 5, 2016

Summary

Scientists are using social media to share research, making agricultural science data more accessible. This study integrates diverse web resources with the AGRIS database to improve research discovery and user experience.

Keywords:

AGRIS Linked Data Recommender Systems SemaGrow Text Categorization Web Crawling

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Area of Science:

Agricultural Science and Technology
Information Science

Background:

Social media is transforming scientific publishing, with researchers sharing findings on blogs and other platforms.
Traditional information systems struggle to manage and integrate this diverse, emerging research data.
Discoverability of scientific literature is challenged by the proliferation of non-traditional publication channels.

Purpose of the Study:

To describe an incremental process for discovering web resources in agricultural science and technology.
To enrich the user experience of the AGRIS website by integrating diverse data sources.
To explore the application of Linked Open Data methodologies for scientific resource discovery.

Main Methods:

Utilizing Linked Open Data methodologies to interlink custom-crawled web resources.
Integrating a wide array of agricultural science web resources with the AGRIS bibliographic database.
Employing the SemaGrow Stack for query federation and data integration.

Main Results:

Successful interlinking of diverse web resources with the AGRIS database.
Demonstration of an incremental approach to discovering and integrating scientific web resources.
Enrichment of the AGRIS website's user experience through enhanced data accessibility.

Conclusions:

Linked Open Data methodologies offer a viable approach to integrating fragmented scientific information.
Information systems must evolve to incorporate diverse data sources for improved research discoverability.
The SemaGrow Stack provides a framework for managing and querying federated scientific data.