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/

9. Unstructured (disorganised) data is chaos

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

18. Research progress is on fire

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

21. Precision & Recall

22. Precision & recall Precision (accuracy / preciseness) How many precise documents were retrieved Recall How many relevant, (but not precise) documents were retrieved

23. Ranking & Re- ranking

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

42. But computational scale is a challenge

43. ‘Curse of Dimensionality’ strikes again

44. How has this challenge been addressed?

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

48. Break the problem down further

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)

89. Create ‘small worlds’

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

93. Wrong pages ranking?

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

101. Check Wikipedia Redirects & merge

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

124. Surfacing Walnuts Walnuts. Dampening Peanuts

125. Multi-search is Multi-dimensional Similarity Search at Scale & across different media types & geo-data

127. Thank you

Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increasingly Small Worlds

Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increasingly Small Worlds

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