Molecular Connections helps improve content discovery and content recommendation for a leading scientific publishing society.

The client is a prestigious publishing society in the physical sciences.

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With rapid digital transformation and evolution in technology, search, and indexing, our client, AIPP, a leading publishing company in physical science, realised the need for modern ways to enrich its content. As technical and scholarly research are constantly evolving and growing, the challenge was to serve content in the best possible way.

The client needed its own gateway for content discovery, which would help in gaining a competitive edge in the market. The client sought a partner who could leverage AI/ML to build a rich ontology, semantically enrich millions of backfiles, and make its content more discoverable.


As a market leader in AI/ML-driven solutions, Molecular Connections’ data experts have gained an enviable reputation for enriching content for global companies by developing solutions in semantic fingerprinting and content classification.

The MC team ensured the ontology built was a manifestation of AIPP’s content. It also incorporated a system wherein feedback and inputs from stakeholders go into the learning system to improve the ontology and the mechanism by which the articles are indexed.

Embracing the challenge of curating 15,00,000 terms and distilling them down to 35,000 terms across 26,000 topics (using our proprietary platforms—MC Lexicon and MC Miner) for creating the ontology, MC subject-matter experts played a major role in enhancing the discoverability of the content.

Further, AIPP has been publishing since 1930, and the nomenclature and language used to describe physics have changed considerably since then. While building the ontology, the MC team had to ensure it references this back in literature and retains the foresight to cover newly emerging areas or new areas that AIPP may move into.

The solution incorporated an automatic high throughput machine learning based semantic fingerprinting of content, adherence to an ontology (keywords vs topics), allowance for user-feedback incorporation, back-file and front-file strategies, batch indexing, re-indexing, API integrations with on-side functionalities, updates, and versioning.

The solution provided polyhierarchical support and semantically indexed 1 million articles with high accuracy. The tech team also put in place a progress monitoring system for Ontology, an ML-based indexer, and APIs, along with versioning and parallel switchover systems for transitioning to new versions.

The result was a comprehensive business application-ready ontology, with a machine learning-based Indexer Module to facilitate ease of use and future maintainability and flexibility.

The key differentiators have been Molecular Connections’ strong proven track record of subject-matter experts in scholarly research and on-time delivery of machine learning-based technology solutions.


Molecular Connections helped the client achieve its business objective by completing the entire project in less than 6 months. The client witnessed improved discoverability and content recommendation, real-time indexing, an expanding reviewer database (Author, Editor, and Referee finder), and contextual advertising specific to selling thesaurus-driven ad space.

For more information on how Molecular Connections’ Al/ML-based models can enhance the value of your content, please reach out to our team.

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