Molecular Connections Delivers an Efficient Automated Indexing Workflow for a Leading Publisher in UK

The client is a top publisher based out of United Kingdom

 

Challenges:

A deluge of data has necessitated secondary publishers to aggregate significantly more content every year.  While outdated indexing workflow systems fail to meet the growing requirement, manual indexing becomes an expensive affair. The client needed an efficient and cost-effective automated workflow.  The challenge was to deal with varying input data structures and data formats for indexing the content and document and to design and iterate domain specific rules for entity extraction and tagging to achieve higher efficiency. The controlled vocabulary used to entity extraction and tagging requires constant updates to keep it up-to-date with the evolving content.

Solution:

We developed an automated workflow (Figure 1) for entity tagging which was rule based and supervised by a regularly updated controlled vocabulary.

The workflow comprised of statistical machine learning modules, entity recognition engines, an up-to-date ontology, and an active learning loop which was also integrated to improve the efficiency of tagging.

Benefits:

The client fully met its business objectives. The chart below depicts the observed trend in improvement of tagging efficiency which can be attributed to both thesaurus enhancements and also Natural Language Processing algorithm fine tuning to suite the requirements of two domains in particular: Production Engineering and Physics. (UCT – Uncontrolled term; CT – Controlled term; CHEM – Chemical index; NUM – Numerical index).

» Faster Indexing: 25% improvement in the indexing time per article

» Robust cloud-based indexing workflow solution, including Quality Control

» Fully automatic indexing achieved for most of the entities and semi-automatic indexing was used to tag the rest

» Continuous improvement via feedback loop

» Thesaurus enhanced by almost 80% within 9 months of operation

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