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Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increasingly Small Worlds
The document discusses the concept of natural semantic SEO, emphasizing the importance of structured, semi-structured, and unstructured data in enhancing search engine understanding. It highlights the role of semantic search in bridging the gap between different data types through improved contextual disambiguation, with a focus on precision and recall in search ranking. Key strategies include leveraging advanced algorithms like BERT and utilizing hierarchical structures to optimize internal linking and enhance relevance in search results.
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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 floatabout 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 theconnection & understanding of 3 types of data STRUCTURED – EASY TO UNDERSTAND SEMI-STRUCTURED – SOME FORM / CATEGORISATION UNSTRUCTURED – LOOSE / DIFFICULT TO UNDERSTAND
- 4. Assisting a reflection ina website as a mirror of ‘real world’ contexts
- 5. True Structured DataIs 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 tableis ‘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 thebest example of semi-structured data & structured data combined
- 8. But it’s estimated80-90% 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.
- 10. The bigger themulti- dimensional data grows the harder the problem
- 11. Because… NUANCE AMBIGUITY LACKOF CONTEXT MANY TYPES OF MEDIA SHEER VOLUME SPARCITY OF MATCHES
- 12. Bellman’s ‘Curse ofDimensionality’
- 14.
- 15. Semantic search seeksto 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’ researchis helping search engines fill ’contextual gaps’ between identifiable entities
- 17. Better understanding is becomingeasier to achieve even without schema markup through natural semantic SEO
- 18.
- 19. Let’s look at thebigger picture
- 20. 4 key areasof search will help us understand these developments Precision & Recall Ranking & Re- ranking Comprehensiveness v Specificity Lexical & Vector Search
- 21.
- 22. Precision & recall Precision(accuracy / preciseness) How many precise documents were retrieved Recall How many relevant, (but not precise) documents were retrieved
- 23.
- 24. Stages of searchengine ranking (at least two) Initial ranking – A shortlist Re-ranking – Grand finale
- 25. Query Comprehensiveness vQuery 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 startedasking the user to extend the query
- 28. e.g. Dresses – Whatdoes the user even want?
- 29. e.g. ‘Wedding Dresses’ –Still under-specified
- 30. Most SEO tools categorise searchterms with a single intent
- 31. But there willbe a descending order of intent probability
- 32. Head Terms WillHave 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 – Rankingvia 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 similaritysearch) Vectors – Mathematical shapes where words with similar meaning ‘live’ near each other Euclidean distance Cosine Similarity
- 37. Lexical Search is Sparse “Bydefinition, 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 / similaritysearch word embeddings are somewhat denser
- 39. The biggest leapforward in natural language understanding is undoubtedly BERT (Devlin, 2018)
- 40. BERT == Separate Contextual Vectors to disambiguate Vector1 - Bank – Money, cash Vector 2 - Bank – River, water
- 42.
- 43.
- 44.
- 45. Use BERT sparingly inthe re-ranking stages of ranking pipelines
- 46. Build teacher &student BERTs
- 47. Build ‘lighter’ naturallanguage models Increase performance dramatically Whilst trading off minimal loss
- 48.
- 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.
- 52. Go smaller still.Break the problem down into sentences & compare cosine similarity of sentence pairs
- 53. Sentence-bert 65 hours withBERT 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-stageretrieval stages on their head
- 57. Feed the re-ranked outputas training data to the ranker to learn better ‘precision’ from the outset
- 58. Machine Learned Dense PassageRetrieval (with hard negatives)
- 59. Hard negatives == ‘Ruleout the obvious incorrects from the start’
- 60. How about clusters ofsimilarity packed together for density?
- 61. How about Sentence-BERT withFAISS (Facebook AI Similarity Search) A library for efficient similarity search and clustering of dense vectors – Facebook Research
- 62. What about Adapt TF:IDFto 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.
- 65. Adapt Navigable SmallWorld (NSW) Graph Algorithms (Small World Networks) with Hierarchical machine learned ‘Similarity Distances’
- 66. ‘Hierarchical Navigable SmallWorld Graphs’ utilizes ML driven tree-graph traversal to identify ‘nearness’ of semantically similar neighbours (Approximate Nearest Neighbours)
- 67. Search for the ‘Approximate NearestNeighbour’ (ANN) across multiple tree levels
- 68. Semantic ‘Small Worlds’ acrossmultiple layers of a connected link graph
- 69. To find themost semantically similar ‘Cluster centroids’ to a query
- 70. Huge progress overallin semantic similarity search
- 71. So why does thismatter to SEOs?
- 72. When Tree Graph ‘Smallworld’ semantic distance matters increasingly…
- 73. And… Zipfian distributionin search demand – Pareto / Power Laws also matters IMPORTANCE
- 74. Walnuts, umbrellas & sidecarsare your friends
- 75. Some peanuts too…but with precision
- 76. In a bowlof mixed nuts…
- 77.
- 78. Search engines areseeking consistent, strong learned confidence in ANN cluster centroids
- 79. But… ‘Approximate Nearest Neighbour’is still just that… Approximate
- 80. Natural Semantic SEOhelps to pinpoint perfect centres whilst also considering Zipfian distribution (importance)
- 81. It’s a reflectionof: How things fit together Their importance as a reflection of real world search demand
- 82. Walnuts of importance mustbe the heart of the target clusters
- 83. Peanuts (lower value) maysteal from the target due to semantic closeness to the centroid
- 84. Skewing the centreof semantic clusters (centroids)
- 85.
- 86. Walnuts == categorical or subcategorical comprehensivenss Peanuts== Specific precision (rareness)
- 87. Internal links can helpor endanger here
- 88. (HNSW / ANN)utilizes a ‘Friends List’ (links)
- 89.
- 90. In Hierarchical Navigable SmallWorlds in SEO Friends list should be closely related internal links
- 91.
- 92. If the centreof the cluster is unclear Google will just keep guessing
- 93.
- 94. Perhaps…Too MANY internallinks / 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 otherpeanuts or walnuts
- 97. TO DO: Internallylink in sectional ‘at scale’ template positons ONLY where there is search volume
- 98. Make sure thepage is important enough (to the real world) for its internal link love
- 99. TO DO: Understandwhen ‘things’ have the same meaning
- 100. Eg. Enterprise architect &business architect Intruder alarm & security alarm Information security & cyber security
- 101.
- 102.
- 103. Also… It’s a‘fine line’ between long tail pages and dynamic search index pruning / excluded
- 104. Too many internallinks from low value (sparse value) pages to each other
- 105. At scale ('Linkedto’) pages not meeting queries (e.g. location + service)
- 106. Using multipliers? ‘Inadvertent’web content SPAM?
- 107.
- 108. You just dropped yoursite off the long tail of the web
- 109. You turned yoursite into an ‘unimportance’ colander
- 110.
- 111. Divide & Rule (Splitcategories ONLY when ready to overflow)
- 112. E.g. - Whenpagination 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 ‘SmallWorlds’ of semantic similarity
- 115. Wrong pages ranking? – Surfacea clear winner
- 116. Where’s the strong centroidof a cluster in ‘no clear winners?’
- 117. Siblings without a parentcategory URLs, connecting competing siblings?
- 118. Your current sitestructure 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’ – Divertlow value + multiplier pages to ‘sidecar pages’ Colours Sizes Teams Services Locations
- 122. Link from sidecars back intoprecise peanuts
- 123. Use internal links Use Hackthe breadcrumb Hack Diverting links via sidecars Connect Show a semantic hierarchical relationship Show Umbrella approaches Use
- 124.
- 125. Multi-search is Multi-dimensionalSimilarity Search at Scale & across different media types & geo-data
- 127.