Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increasingly Small Worlds
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Structured data accounts for only a small part of the web and the problem grows as the volume of the online content grows. Schema markup is a drop in the ocean to help with this. However, things are being addressed in the natural language research space in the form of dense retrieval and other developments such as Sentence BERT and FAISS. Utilising heuristics such as umbrellas and sidecar pages will help to send clues and assist with ensuring search engines rank the right pages from your sites for SEO
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Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increasingly Small Worlds
- 1. ‘Natural Semantic SEO’ - Surfacing walnuts in densely represented, ever increasingly small worlds – ‘Umbrella’ and ‘Sidecar’ Approaches - Dawn Anderson Bertey
- 2. Semantic SEO – So many definitions float about IT’S ALL ABOUT SYNONYMS IT’S ALL ABOUT TOPICS IT’S ALL ABOUT INTENT IT’S ALL ABOUT ‘THINGS’ NOT ‘STRINGS’ IT'S ABOUT ENTITIES
- 3. Simplified… it’s the connection & understanding of 3 types of data STRUCTURED – EASY TO UNDERSTAND SEMI-STRUCTURED – SOME FORM / CATEGORISATION UNSTRUCTURED – LOOSE / DIFFICULT TO UNDERSTAND
- 4. Assisting a reflection in a website as a mirror of ‘real world’ contexts
- 5. True Structured Data Is Easy to Disambiguate Resides in a database Is quantifiable Can be tabular data Is well organised Is likely stored in rows & columns Has relational keys mapped to fields
- 6. A simple table is ‘true structured data’ (a relational database) Column ID Column ID Column ID Column ID Column ID Column ID Row ID Blah Blah Blah Blah Blah Blah Row ID Blah Blah Blah Blah Blah Blah Row ID Blah Blah Blah Blah Blah Blah Row ID Blah Blah Blah Blah Blah Blah Row ID Blah Blah Blah Blah Blah Blah
- 7. Wikipedia is the best example of semi-structured data & structured data combined
- 8. But it’s estimated 80-90% of the world’s data is unstructured (2021) Source: https://venturebeat.com/2021/07/22/why-unstructured-data-is-the-future-of-data-management/
- 10. The bigger the multi- dimensional data grows the harder the problem
- 11. Because… NUANCE AMBIGUITY LACK OF CONTEXT MANY TYPES OF MEDIA SHEER VOLUME SPARCITY OF MATCHES
- 12. Bellman’s ‘Curse of Dimensionality’
- 14. But… there is good news
- 15. Semantic search seeks to help with tying structured data & unstructured data together – Disambiguates context Unstructured data (text in web content) Structured data (Knowledge graphs / knowledge repositories)
- 16. ‘Natural language’ research is helping search engines fill ’contextual gaps’ between identifiable entities
- 17. Better understanding is becoming easier to achieve even without schema markup through natural semantic SEO
- 19. Let’s look at the bigger picture
- 20. 4 key areas of search will help us understand these developments Precision & Recall Ranking & Re- ranking Comprehensiveness v Specificity Lexical & Vector Search
- 22. Precision & recall Precision (accuracy / preciseness) How many precise documents were retrieved Recall How many relevant, (but not precise) documents were retrieved
- 24. Stages of search engine ranking (at least two) Initial ranking – A shortlist Re-ranking – Grand finale
- 25. Query Comprehensiveness v Query Specificity
- 26. The Under-specified Query == Generic head terms Mostly search engines have no clue what the user wants with broad, generic terms
- 27. They’ve even started asking the user to extend the query
- 28. e.g. Dresses – What does the user even want?
- 29. e.g. ‘Wedding Dresses’ – Still under-specified
- 30. Most SEO tools categorise search terms with a single intent
- 31. But there will be a descending order of intent probability
- 32. Head Terms Will Have Multiple Intents Most tools only categorise head terms with a single intent But this is not the case Intent will have a descending order of predictable probability Intent shifts often and with temporal patterns / predictability Comprehensive search results (Search Result Diversification) seeks to meet all of these possible intents probably predicting intents in descending order of likelihood)
- 33. Winner? Head – Ranking via comprehensiveness (meet ALL the needs of the under-specified query) (within the collection) Tail – Ranking by rareness / specificity / precision (meet that one SPECIFIC need with rarity)
- 34. Lexical & Semantic/ Similarity Search
- 35. Lexical Search – ‘Bag of Words’ (eg: TF:IDF & BM25 algorithms) (Word matches)
- 36. Semantic Search (Vector similarity search) Vectors – Mathematical shapes where words with similar meaning ‘live’ near each other Euclidean distance Cosine Similarity
- 37. Lexical Search is Sparse “By definition, a sparse matrix is called “sparse” if most of its elements are zero. In the bag of words model, each document is represented as a word-count vector.” Source: https://sebastianraschka.com/faq/docs/bag- of-words-sparsity.html
- 38. Vector / similarity search word embeddings are somewhat denser
- 39. The biggest leap forward in natural language understanding is undoubtedly BERT (Devlin, 2018)
- 40. BERT == Separate Contextual Vectors to disambiguate Vector 1 - Bank – Money, cash Vector 2 - Bank – River, water
- 45. Use BERT sparingly in the re-ranking stages of ranking pipelines
- 46. Build teacher & student BERTs
- 47. Build ‘lighter’ natural language models Increase performance dramatically Whilst trading off minimal loss
- 49. use BERT on ‘passages’ of documents Put Put the document back together Break down Break down a document and turn it into passages Passage Passage indexing / ranking & re-ranking
- 50. Fight back against ‘The Curse of Dimensionality’
- 51. Dense Retrieval
- 52. Go smaller still. Break the problem down into sentences & compare cosine similarity of sentence pairs
- 53. Sentence-bert 65 hours with BERT to find two most similar sentences from 10,000 sentences using normal BERT / RoBERTa model Same task around 5 seconds with Sentence- bert
- 54. Siamese BERT Networks – Two BERT encoders working together
- 55. Siamese BERT - Utilising cosine similarity of two output sentences
- 56. Turn the multi-stage retrieval stages on their head
- 57. Feed the re-ranked output as training data to the ranker to learn better ‘precision’ from the outset
- 58. Machine Learned Dense Passage Retrieval (with hard negatives)
- 59. Hard negatives == ‘Rule out the obvious incorrects from the start’
- 60. How about clusters of similarity packed together for density?
- 61. How about Sentence-BERT with FAISS (Facebook AI Similarity Search) A library for efficient similarity search and clustering of dense vectors – Facebook Research
- 62. What about Adapt TF:IDF to include topic classes (C-TF-IDF)
- 63. BERTopic & C-TF-IDF (Class based Frequency) Utilises topical classes / concepts packed with term frequency / inverse document frequency Builds topic clusters as an alternative to simple word count in proportion to document length
- 64. Take similarity seeking beyond the document
- 65. Adapt Navigable Small World (NSW) Graph Algorithms (Small World Networks) with Hierarchical machine learned ‘Similarity Distances’
- 66. ‘Hierarchical Navigable Small World Graphs’ utilizes ML driven tree-graph traversal to identify ‘nearness’ of semantically similar neighbours (Approximate Nearest Neighbours)
- 67. Search for the ‘Approximate Nearest Neighbour’ (ANN) across multiple tree levels
- 68. Semantic ‘Small Worlds’ across multiple layers of a connected link graph
- 69. To find the most semantically similar ‘Cluster centroids’ to a query
- 70. Huge progress overall in semantic similarity search
- 71. So why does this matter to SEOs?
- 72. When Tree Graph ‘Small world’ semantic distance matters increasingly…
- 73. And… Zipfian distribution in search demand – Pareto / Power Laws also matters IMPORTANCE
- 74. Walnuts, umbrellas & sidecars are your friends
- 75. Some peanuts too… but with precision
- 76. In a bowl of mixed nuts…
- 77. Walnuts rise to the top
- 78. Search engines are seeking consistent, strong learned confidence in ANN cluster centroids
- 79. But… ‘Approximate Nearest Neighbour’ is still just that… Approximate
- 80. Natural Semantic SEO helps to pinpoint perfect centres whilst also considering Zipfian distribution (importance)
- 81. It’s a reflection of: How things fit together Their importance as a reflection of real world search demand
- 82. Walnuts of importance must be the heart of the target clusters
- 83. Peanuts (lower value) may steal from the target due to semantic closeness to the centroid
- 84. Skewing the centre of semantic clusters (centroids)
- 85. But peanuts do matter too
- 86. Walnuts == categorical or subcategorical comprehensivenss Peanuts == Specific precision (rareness)
- 87. Internal links can help or endanger here
- 88. (HNSW / ANN) utilizes a ‘Friends List’ (links)
- 90. In Hierarchical Navigable Small Worlds in SEO Friends list should be closely related internal links
- 91. But…
- 92. If the centre of the cluster is unclear Google will just keep guessing
- 94. Perhaps…Too MANY internal links / Too MANY internal links with similar long tail anchors to similar semantic distance pages
- 95. Competing with Self on ‘too semantically similar’ linked pages
- 96. Peanuts semantically fighting with other peanuts or walnuts
- 97. TO DO: Internally link in sectional ‘at scale’ template positons ONLY where there is search volume
- 98. Make sure the page is important enough (to the real world) for its internal link love
- 99. TO DO: Understand when ‘things’ have the same meaning
- 100. Eg. Enterprise architect & business architect Intruder alarm & security alarm Information security & cyber security
- 102. On redirects: Keep the semantic distance short
- 103. Also… It’s a ‘fine line’ between long tail pages and dynamic search index pruning / excluded
- 104. Too many internal links from low value (sparse value) pages to each other
- 105. At scale ('Linked to’) pages not meeting queries (e.g. location + service)
- 106. Using multipliers? ‘Inadvertent’ web content SPAM?
- 107. Peanut dilution
- 108. You just dropped your site off the long tail of the web
- 109. You turned your site into an ‘unimportance’ colander
- 110. Overflow SEO
- 111. Divide & Rule (Split categories ONLY when ready to overflow)
- 112. E.g. - When pagination gets indexed consider an overflow split
- 113. As Content / Offerings Grow… ‘Overflow’ Part of page Overflows to Overflows to Subcategory Category Leaf page Overflows to
- 114. Build increasingly ‘Small Worlds’ of semantic similarity
- 115. Wrong pages ranking? – Surface a clear winner
- 116. Where’s the strong centroid of a cluster in ‘no clear winners?’
- 117. Siblings without a parent category URLs, connecting competing siblings?
- 118. Your current site structure won’t allow it you say?
- 119. Use ‘Umbrella & Sidecar’ approaches – Build & connect intents upwards & sideways
- 120. Umbrella Approaches add intent hubs ‘above’ & are navigational / triage systems by nature Connect to Primary suspected intent Connect to Secondary intent Connect to Secondary intent Connect to Secondary intent Connct to Secondary intent Connect to Secondary intent Connect to Secondary intent
- 121. ‘Sidecar pages’ – Divert low value + multiplier pages to ‘sidecar pages’ Colours Sizes Teams Services Locations
- 122. Link from sidecars back into precise peanuts
- 123. Use internal links Use Hack the breadcrumb Hack Diverting links via sidecars Connect Show a semantic hierarchical relationship Show Umbrella approaches Use
- 125. Multi-search is Multi-dimensional Similarity Search at Scale & across different media types & geo-data
- 127. Thank you