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BrightonSEO March 2021 | Dan Taylor, Image Entity Tags

My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.

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BrightonSEO March 2021 | Dan Taylor, Image Entity Tags

  1. 1. Using Image Category Labels For Content Optimization Dan Taylor // SALT.agency
  2. 2. Head of Research & Development @ SALT.agency • 2018 TechSEO Boost Innovation Award • Search Engine Journal Author • Search Engine Journal Top 140 (“18, “19, “20) • SEMrush Top Author • https://salt.agency (#Hiring #RemoteUK) • https://hreflangchecker.com • https://sloth.cloud • https://dantaylor.online These slides: https://dantaylor.online/brighton2021 dantaylor.online // @taylordanrw
  3. 3. Today Today, I’ll be talking about how we can use Google’s imaging labels and entity classification systems as a method of keyword and topic research, to improve our content. “Improve our content” Rankings? Conversion? User Happiness & Experience Forecasting? Enhanced topic research to identify/reaffirm connections between topics we’re exploring. But first, a little back story... 3 dantaylor.online // @taylordanrw
  4. 4. Semantic Search & SEO Semantics in SEO isn’t a new concept, or new to Google. The first mention of Semantic search came in a 1999 Sergey Brin patent. If you’re able to understand entities, then you can gain an insight into how Google looks at individual webpages, websites (as a cohort of pages), and then interprets the information contained therein. Topic clusters have nothing to do with Hummingbird, and RankBrain. #Myth 4 dantaylor.online // @taylordanrw
  5. 5. “ So, when someone searches. Google may compare the SERPs they receive from the original query to augmented query results based on previous searches using the same query terms or synthetic queries. This evaluation against augmentation queries is based upon which search results have received more clicks in the past. Google may decide to add results from an augmentation query to the results for the query searched for to improve quality scores and the overall search results. 5 Bill Slawski dantaylor.online // @taylordanrw
  6. 6. Sphere of influence As SEOs, we can only impact the initial phase, and our efforts are then see as rewards during the indexing and ranking phases. 6
  7. 7. What is an image entity tag/category label?
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  9. 9. “ Per some embodiments, object recognition technology is used to annotate images stored in databases or harvested from Internet web pages. The annotations may identify who and/or what is contained in the images. 9 US Patent: 10,534,810 - Ran El Manor and Yaniv Leviathan, Google LLC dantaylor.online // @taylordanrw
  10. 10. Images & The Knowledge Graph Back in 2012, when Google started the Knowledge Graph they included a phrase that “(Google) can learn from images of real-world objects” We know from other patents that Google annotates images as part of the mechanisms to grow and increase connections within the Knowledge Graph between entities (object and attribute). It’s logical that this would be a two way process; with learnings from image attribute entity recognition feeding into the overall knowledge sphere, and vice versa. 10 dantaylor.online // @taylordanrw
  11. 11. Chuck Rosenberg, Image Search Team - 2013 11
  12. 12. 5 Query Satisfaction Objectives 1. Continuation of neural mapping processes to provide better results for ambiguous queries 2. Improve needs met results for queries with multiple common (strong) interpretations 3. Advancement beyond the “written word” in terms of entity relationship understanding 4. Improve the results (in terms of relevancy) for general image search 5. [Internal] work by Google to test and train against new queries/tests to perform updates/reaffirm data sets/improve data sets/entity relationships 12 dantaylor.online // @taylordanrw
  13. 13. Scores We know from patent analysis (hail Bill Slawski) that Google uses association scores (the score to quantify the relationship between two entities within a database). We know that Google then has entity classes, and subclasses. Entities may be associated with one or more entity attributes and/or object attributes. This then begs the question of how association and confidence scores and impact the tangible output (the image filter tags). 13 dantaylor.online // @taylordanrw
  14. 14. “ An association score may reflect a likelihood or degree of confidence that an attribute, attribute value, relationship, class hierarchy, designated context class, or other such association is valid, correct, and/or legitimate. In some embodiments, for example, an association score may reflect a degree of relatedness between two entities or a context and an entity. 14 US Patent 10,198,491, a lot of authors, Google LLC dantaylor.online // @taylordanrw
  15. 15. Illustration from the patent 10,534,810 15
  16. 16. Mapping Entity Relations From the bear example in the patent we can begin to map out a relationship between various entities. How we define or connect the relationships (for the purpose of improving our user happiness) is down to our own, logical interpretation - aka common sense. Google does this through machine learning, and combing the information from image annotations with prior knowledge from the Knowledge Graph. 16 dantaylor.online // @taylordanrw
  17. 17. Illustration from the patent 10,534,810 17 Node Edge
  18. 18. 18 dantaylor.online // @taylordanrw
  19. 19. Similar To Topic Mapping 19
  20. 20. Query Disambiguation 20
  21. 21. Ok, great. So why/how can I use this?
  22. 22. Entities & Content Optimization Remember that Google is a machine. When a web crawler looks at an entity, it does so as a node - and then works to identify relationships between those entities. They are trained (ML) to look for entities, and identify context and relationships. We optimise our internal linking between webpages (that house related content) like nodes & edges… (IMO) Google can also ascertain relevance between documents on a single domain, without links - but they help. 22 dantaylor.online // @taylordanrw
  23. 23. Scalable Extraction We can use Python to extract the tags from Google image search - meaning we can scale this process. With simple extraction around a small, initial set of search queries we can begin to identify and unearth potential relationships between concepts and entities (various classes) that aren’t demonstrated in traditional keyword research tools working off queries alone. Google likes to update it’s HTML, so you will need to keep updating your scrapers... 23 dantaylor.online // @taylordanrw
  24. 24. [Cyprus Holidays] 24 dantaylor.online // @taylordanrw BEACH BEACH
  25. 25. [Cyprus Holidays] Image Tags ● All inclusive ● Ayia Napa ● Cheap ● Protara ● Beach ● 5 Star ● Nissi Beach ● Larnaca ● Coral Bay ● Limassol 25 dantaylor.online // @taylordanrw ● Family ● Luxury ● Paphos ● Turkish ● Summer ● Resort ● Waterpark ● Grecian Park Hotel ● Electra Village ● South ● Anastasia ● Map ● Elysium ● Nestor hotel ● Famagusta ● Pernera ● Night life Place/Location -- Hotel/Hotel Modifier -- Holiday Modifier
  26. 26. Result #1 holidayhypermarket.co.uk/destinations/cyprus ● All inclusive ● Ayia Napa ● Cheap ● Protara ● Beach ● 5 Star ● Nissi Beach ● Larnaca ● Coral Bay ● Limassol 26 dantaylor.online // @taylordanrw ● Family ● Luxury ● Paphos ● Turkish ● Summer ● Resort ● Waterpark ● Grecian Park Hotel ● Electra Village ● South ● Anastasia ● Map ● Elysium ● Nestor hotel ● Famagusta ● Pernera ● Night life
  27. 27. Result #54 scottdunn.com/cyprus ● All inclusive ● Ayia Napa ● Cheap ● Protara ● Beach ● 5 Star ● Nissi Beach ● Larnaca ● Coral Bay ● Limassol 27 dantaylor.online // @taylordanrw ● Family ● Luxury ● Paphos ● Turkish ● Summer ● Resort ● Waterpark ● Grecian Park Hotel ● Electra Village ● South ● Anastasia ● Map ● Elysium ● Nestor hotel ● Famagusta ● Pernera ● Night life
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  32. 32. https://scottmathson.com/blog/2020/04/23/cloudflare-dns-content-hub-case-study-pt-1-seo-rankings/ 32
  33. 33. 33 Thank you! @taylordanrw https://salt.agency

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