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 Education sector.
InLinks analyzed the page content of the top 10 Google results (in the UK Market) ranking for the phrase university courses 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 Education sector
The results showed that 34.6% of entities seen on the results in the Education sector SERPS (search engine Results pages) were positively identified by Google.
This compares to 20.3% 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.
|Education Industry||Google Analysis||InLinks Analysis|
|Avg. Number of words per page||538|
|Avg. Number of Topics per page||13.6||39.3|
|Benchmark - Avg. nb of entities/page (all sectors)||9.5||47.8|
|Of which, Topic types are:|
|- Cities & geo. areas||43||48|
Google’s Search API returned URLs for the following sites competing for this phrase:
#open.ac.uk, #ox.ac.uk, #london.ac.uk, #kent.ac.uk, #manchester.ac.uk, #studyin-uk, #thecompleteuniversityguide, #theuniguide, #undergraduate.study.cam.ac.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 University, Student, Learning, College, Professional certification.
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 Education market, based on its NLP algorithms remains limited at 34.6% for this industry. Businesses either need to improve their schema or make their content more understandable by Google to improve its level of understanding.
Coronavirus, Higher Education Statistics Agency
OpenLearn, Twitter, Facebook, British Forces Post Office, Welsh language
Mechanical engineering, Civil engineering
Open University, Master's degree, Doctor of Philosophy, OpenLearn, England, Facebook, Twitter, Welsh language, British Forces Post Office
University of Oxford, United Kingdom, Course (education)
University of Manchester, Bachelor of Science, United Kingdom, Manchester, UCAS, Hotcourses Group, Facebook, Royal charter, Twitter, Sina Weibo, Studyportals
United Kingdom, Aberystwyth University, Edge Hill University, Dublin City University, Glasgow Caledonian University, Goldsmiths, University of London, Harper Adams University, Heriot-Watt University, Cardiff Metropolitan University, Canterbury Christ Church University, Brunel University London, Birmingham City University, Bangor University, Cranfield University, Leeds Beckett University, University of Manchester, Lancaster University, Newcastle University, London Metropolitan University, Maynooth University, Middlesex University, Manchester Metropolitan University, Loughborough University, Mechanical engineering, King's College London, Queen Mary University of London, Glasgow School of Art, Robert Gordon University, Teesside University, Sheffield Hallam University, Swansea University, University of Oxford, Regent Street, University of Gloucestershire, University of Bedfordshire, English language, Manchester, University of the West of England, Bristol, University of Warwick
Derby, Greater London, Cambridge, West Midlands (county), Northamptonshire, Leicester, Derbyshire, South Tyneside, North Tyneside, London Borough of Waltham Forest, Stockton-on-Tees, Luton, Norfolk, Peterborough, Richmond, London, London Borough of Wandsworth, Sunderland, Hartlepool, Middlesbrough, Yorkshire, North Yorkshire, Yorkshire and the Humber, North Wales, North Lincolnshire, Bath and North East Somerset, Bracknell, Blackburn, Worcestershire, Walsall, Warwickshire, Wolverhampton, Sandwell, Huddersfield, Kirklees, Rotherham, Bournemouth, Swindon Village, Powys, Birmingham, Wigan, Warrington, Metropolitan Borough of Wirral, Bury, Greater Manchester, Cheshire, Crewe, Cumbria, Rochdale, Liverpool, Hampshire, Milton Keynes
London, Bachelor of Science, Institute, Paris, Bachelor of Laws, Postgraduate certificate, Postgraduate diploma, Royal Holloway, University of London, English language, Queen Mary University of London, Certificate of Higher Education, London School of Economics, Financial accounting
University of Kent, Kent