Semantics and Search by Upasna Gautam at PubCon Austin 2018
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My presentation covers what semantic search is, how it and the Google algorithm has evolved, and how can you make it work for your SEO strategy.
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Semantics and Search by Upasna Gautam at PubCon Austin 2018
- 1. #pubcon Semantics and Search Presented by: Upasna Gautam aka Pas
- 2. #pubcon Objectives •What is semantic search? •What is NOT semantic search? •How does Google make it work? •How can you make it work?
- 4. #pubcon SEO: Then & Now Back then: •Keyword-focused: • Text retrieval system relied on exact match keywords • Weighted documents by keyword frequency •Unable to distinguish synonyms and homographs • Synonym: Words that share the same meaning (e.g. car and automobile) • Homograph: More than one meaning depending on context (e.g. “charge)
- 5. #pubcon SEO: Then & Now Now: •Driven by intent and context •Provide relevant answers to complex and vague queries
- 6. #pubcon SEO: Then & Now
- 7. #pubcon SEO: Then & Now Now: •“best vegan tacos austin” •“late night texmex delivery austin” •“best happy hour margaritas 78701”
- 8. #pubcon SEO: Then & Now Now: Search Experience Optimization
- 9. #pubcon SEO: Then & Now What enabled search engines to understand our queries on an intelligent level?
- 11. #pubcon What is Semantic Search Semantics: A branch of linguistics that studies the relationship between words and sentences and their actual meanings. Semantic Search: The improvement of search accuracy by understanding intent and context, using various on-site elements to crawl, index, and serve relevant results.
- 12. #pubcon What is Semantic Search •Entity Optimization •Knowledge Graph •Structured Data •Information Architecture •Co-occurrence and Clustering
- 13. #pubcon What is Semantic Search: Entity Optimization Paul Haahr – Google Ranking Engineer – SMX 2016
- 14. #pubcon What is Semantic Search: Knowledge Graph •Understands relationships between things •Stores and understands the intelligence between different entities •Not just a catalog of objects, but a data model for inter-relationships
- 15. #pubcon What is Semantic Search: Structured Data •Google is a data-driven machine that needs to be fed in order for it to learn •Feed it structured data – it’s a piece of intelligence the crawler uses to build semantic relevance and authority •This is how entities are indexed!
- 16. #pubcon What is Semantic Search: Information Architecture •Allows for a crawler to clearly understand content and how it’s connected •Provide a clear and hierarchical path of information •Lends to a good UX •The RIGHT approach is the most LOGICAL approach •Must read: Information Architecture for the World Wide Web [3rd Edition, by Peter Morville]: https://www.amazon.com/Information-Architecture-World-Wide- Web/dp/0596527349
- 17. #pubcon What is Semantic Search: Co-Occurrence and Clustering Word Co-Occurrence Clustering • Generates topics from words frequently occurring together Weighted Bigraph Clustering • Uses URLs from Google search results to induce query similarity and generate topics The combination of these two methods demonstrated greater usefulness and accuracy when compared to Latent Semantic Analysis. Read the patent here: https://pdfs.semanticscholar.org/dcf7/05ba07ee1b73fda0c94e9d01b2474173e470.pdf
- 18. #pubcon What is Semantic Search: Co-Occurrence and Clustering Word Co-Occurrence • A set of words anchors serve as initial topics, which are then generalized to other words co-appearing with the same queries. • Topics are created using hierarchical clustering on query similarity, which measures to what extent two queries agree on their intersections with the list of words in each topic. Bigraph Clustering • Uses organic results to create a bigraph with a set of queries and a set of URLs as nodes. Weights of the graph are computed with the impression and click data. • Bigraph clustering works very well even if the queries do not share common words
- 19. #pubcon Latent Semantic Indexing Is NOT Semantic Search
- 20. #pubcon BUT…
- 21. #pubcon • Learning the mathematical relevance helps to understand search on a functional level • LSI uses Singular Value Decomposition which is a linear algebraic factorization for many of our modern algorithms • It is not a way to “do SEO” • LSI KEYWORDS ARE NOT A THING
- 22. #pubcon Latent Semantic Indexing Latent Semantic Indexing (LSI): •Mathematical algorithm based on Singular Value Decomposition (SVD) •Text indexing and retrieval method •How terms and concepts are related
- 23. #pubcon Latent Semantic Indexing •LSI works by projecting a large multi- dimensional space down into a smaller number of dimensions •Semantically similar words get bunched together •Boundary blurring allows LSI to go beyond exact keyword matching
- 24. #pubcon Latent Semantic Indexing •LSI uses Singular Value Decomposition (SVD) to decompose this matrix •Preserves information about relative distances between document vectors •Collapsed into smaller dimensions •Information is lost and words are superimposed on one another
- 25. #pubcon Latent Semantic Indexing •Noise reduction •Reveal similarities that were latent •Similar terms become more similar, while dissimilar things remain distinct This method is a widely used technique to unveil latent themes in text data, as these models learn the hidden topics by understanding document level word co-occurrence patterns.
- 26. #pubcon Latent Semantic Indexing Short texts, such as search queries, tweets or instant messages suffer from data sparsity, which causes problems for traditional topic modeling techniques. Unlike proper documents, short text snippets do not provide enough word counts for models to learn how words are related and to disambiguate multiple meanings of a single word. *This is why the binary co-occurrence/clustering model works better*
- 28. #pubcon Key Takeaways •Craft and optimize content for topics and concepts, not just keywords •Use structured data to feed crawler the semantic intelligence it needs to understand your site better •Align the information architecture of your website to the consumer journey •Navigation, sitemaps, page structure, content organization •Stop saying/using “LSI keywords” •The best approach is the most logical approach!
- 29. #pubcon The End