Molecular Connections developed a custom-built, high-throughput, and multifaceted Keyword Recommendation Service (KRS) for IFIS, a not-for-profit publisher specializing in food and health sciences. The KRS utilizes advanced deep learning techniques, including semantic similarity algorithms, contextual vectorizations, and bi-directional transformers, to analyze the Food Science and Technology Abstracts (FSTA) database. This enables the system to suggest relevant keywords that match the titles and abstracts of scholarly articles, assisting researchers in selecting appropriate terms for their work
Benefits & outcomes
- Enhanced discoverability of research through improved keyword suggestions.
- Assistance to researchers in selecting appropriate thesaurus terms, reducing the guesswork in keyword selection.
- A reported 40% increase in website traffic, indicating improved user engagement and content visibility.
- Support for SEO efforts, leading to more impactful content promotion.
Notes & quotes
“The system returns keyword data from the top 100 similar articles identified by an artificial intelligence algorithm.”
“Bi-directional transformers were used as an attempt to construct and learn dependency grammars.”
“The KRS Semantic resulted in better submission, and it not only provided relevant and reliable search results leading to impactful content and promotion, but also helped users with SEO.”
“There was a 40 percent jump in traffic.”
Publisher / company: Molecular Connections
Date of case study: 01/01/2025
Source: https://mcpublishing.co.in/case_studies/krs-ifs/