Google uses Natural Language Processing (NLP) routines to convert its understanding of text into topics and concepts (Entities). We looked out how much understanding Google misses using this approach in the Health sector.
InLinks analyzed the page content of the top 10 Google results (in the UK Market) ranking for the phrase Kawasaki disease and compared the Named Entities recognized by Google’s NLP API with the proprietory routines designed by Inlinks to uncover gaps in Google’s machine learning in the Health sector
The results showed that 16.1% of entities seen on the results in the Health sector SERPS (search engine Results pages) were positively identified by Google.
This compares to 19.1% average across all analyzed industry sectors.
Different sectors tend to be analyzed with a different degree of accuracy by the search engines. This stems from two main challenges.
|Health Industry||Google Analysis||InLinks Analysis|
|Avg. Number of words per page||841|
|Avg. Number of Topics per page||7.4||46.1|
|Benchmark - Avg. nb of entities/page (all sectors)||9||48|
|Of which, Topic types are:|
|- Cities & geo. areas||16||14|
Google’s Search API returned URLs for the following sites competing for this phrase:
#bestpractice.bmj, #bbc, #chfed.org.uk, #gosh.nhs.uk, #nhs.uk, #societi.org.uk, #vasculitis.org.uk
The texts of each page are then sent to Google’s NLP API, in order to determine which entities are identified by the search engine. These are important for search since Google is then able to link these to its Knowledge Graph to feed its services including Google Discover, Google search, Voice Search and Google News. (Although, correct identification does not guarantee inclusion in these results)
Here is first of all the synthesis of the results returned by Google:
Here are some errors in the categorization of entities:
By understanding where Google is failing to recognize important concepts within the industry, there is an opportunity for companies in the sector to write clearer content that Google can better understand.
Another option is to explicitly state these concepts in Webpage schema for machine learning algorithms to take into account. This would require using Schema.org/WebPage and the "about" and mentions" properties for important concepts such as Symptom, Complication (medicine), Therapy, Lip, Fever, Human eye, Disease, Medicine, Knee effusion.
Internal linking of topics topages dedicated to each important topic will also help to reduce cannibalisation of content in Google’s understanding of contect within your content.
Google's understanding of the Health market, based on its NLP algorithms remains limited at 16.1% for this industry. Businesses either need to improve their schema or make their content more understandable by Google to improve its level of understanding.
National Health Service, United Kingdom, Immunoglobulin therapy, England
Great Ormond Street Hospital, Japan, United Kingdom
Kawasaki disease, Tomisaku Kawasaki, United Kingdom, Japan
National Health Service, South Korea, Japan, Asia
Kawasaki disease, Coronavirus disease 2019, Royal College of Paediatrics and Child Health, Winthorpe, Nottinghamshire, Newark (parish), Nottinghamshire, England, Conwy
Facebook, Twitter, Pinterest, WhatsApp, LinkedIn, Facebook Messenger, BBC News (TV channel), News, Scientific Advisory Group for Emergencies, England, China, University of Exeter, Conwy, Northern Ireland, United Kingdom, University College London, French Alps, Netherlands, Italy, Iceland, South Korea, Shenzhen
Tomisaku Kawasaki, Kawasaki disease, Cardiology, Fundraising Regulator, Warfarin, Nifedipine, Propranolol
Kawasaki disease, United Kingdom, Coronavirus disease 2019, Strawberry, Rash, Ireland, Nottinghamshire, Newark (parish), Winthorpe, Nottinghamshire, England, Conwy, Coronavirus, Royal College of Paediatrics and Child Health
The BMJ, Medical diagnosis, C-reactive protein, United States, Carlsbad, California, California, BMJ (company), Agency for Science, Technology and Research, Government of Singapore