BrightonSEO March 2021 | Dan Taylor, Image Entity Tags

The document discusses Google's ML APIs versus OpenAI's APIs and their applications for SEO and digital marketing tasks. It provides examples of how natural language processing APIs from Google and OpenAI can be used for tasks like text analysis, sentiment analysis, document classification, translation and content transformation. While both Google and OpenAI APIs are useful, the document recommends choosing the right API for each specific task based on its capabilities and limitations in order to get the best results.

Avoid the most common SEO issues, challenges and mistakes by going through this presentation with tips, criteria and tools to use independently of your online store Web platform, and grow your organic search results

A look at search-related patents from Google that people who do SEO may be interested in learning about

Patrick Stox gives a presentation on how search works. He discusses how Google crawls and indexes websites, processes content, handles queries, and ranks results. Some key points include: Google's crawler downloads pages and files from websites; processing includes duplicate detection, link parsing, and content analysis; queries are understood through techniques like spelling correction and query expansion; and search results are ranked based on numerous freshness, popularity, and relevancy signals.

There are three main components of information retrieval systems: query understanding, document-query relevance understanding, and document clustering and ranking. The path from a search query to a search document involves several steps like query parsing, processing, augmenting, scoring, ranking, and clustering. Query understanding is where search engine optimization (SEO) begins, while document creation and ranking are other areas where SEO is applied. Cranfield experiments in the late 1950s helped develop the concept of a "search query language" which is different from the language used in documents. Formal semantics and components like tense, aspect, and mood can help machines better understand human language for information retrieval tasks.

This document summarizes how Google search results are evolving to include more semantic data through direct answers, structured snippets, and rich snippets. It provides examples of direct answers being extracted from authoritative sources using natural language queries and intent templates. It also discusses how including structured data like tables, schemas, and markup can help search engines understand and display page content in a more standardized way. While knowledge-based trust is an interesting concept, current search ranking still primarily relies on link analysis and does not consider factual correctness.

This document discusses digital marketing strategies focused on establishing authority through valuable, timeless content. It recommends creating content such as articles, videos, and academic papers on topics that will remain relevant for years to establish expertise. Creating a steady stream of high-quality content over time builds an online presence and credibility without major risks of losses, and may lead to job offers, clients, or other opportunities. It provides examples of interactive dashboards and open-source software that gained popularity and users by continuously publishing improvements and documentation without needing to rely on things like resumes or company profiles.

This document contains the transcript of a presentation about incorporating machine learning into internal linking audits. The presentation discusses analyzing a website's internal link structure using machine learning techniques like topic modeling and fuzzy matching to identify opportunities for new or improved internal links. It provides a 6-step process for discovery, analysis, clustering content by topic, identifying link opportunities, prioritizing where to link, and measuring the impact of implemented links. The goal is incremental improvements to internal linking that can boost SEO over time through better content organization and discoverability.

Maximize cost-effective SEO implementation and achieve SEO success with SEO audits that get implemented: * Make your SEO audits solutions focused to develop action driven recommendations * Prioritize your SEO recommendations based on impact and effort, with SEO low-hanging fruits * Establish ongoing actions to prevent new SEO issues & leverage opportunities during the process * Connect each of your SEO recommendations to SMARTER SEO goals * Format SEO recommendations to facilitate actionability and collaboration * Develop frequent recommendations reviews & tests to keep them relevant and impactful

The document discusses optimizing product listing pages (PLPs) on ecommerce websites. It begins with the author describing their experience finding a website with little obvious "tech debt" issues to address. They then analyze which page templates drive the most revenue, finding PLPs account for 60% of organic revenue. The author breaks down PLPs into individual components and suggests prioritizing optimization of filters and internal linking. They argue for considering metrics beyond just search volume, like user behavior and conversion data, when deciding which page variants to focus on.

This document discusses how to control Googlebot's crawling of a website. It notes that Googlebot often does not crawl all pages of large websites due to limited crawl budgets. It recommends analyzing website logs and other metrics like pages crawled, indexed, ranked, impressions, clicks and conversions to understand Googlebot's behavior. The key factors that influence Googlebot are described as the "magic triangle" of links, content, and technical aspects. All three need attention to help Googlebot fully crawl and index a website.

Learn how to develop successful SEO reports to deliver to stakeholders and decision makers that effectively communicate the SEO process goals and influence actions.

This is a guide for machine learning for beginners, tailored to the SEO industry, aimed at breaking down the challenges that hold us back from experimenting, the breakdown of machine learning's main characteristics to help us understand how to implement it a bit better, and the ways we can embed advanced technology into our daily practice.

Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries. The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together. Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more. Themes of words contain the word probability like in Continues Bag of Window. The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary. Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization. The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel. Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.

Patrick's Brighton SEO talk on using machine learning for technical SEO and how to automate many things.

The document discusses keyword research and topic modeling in the semantic web. It covers identifying named entities, adding schema markup to pages, and verifying listings on Google My Business. It also discusses using context and related phrases to improve search engine optimization, including looking at knowledge bases, disambiguations pages, and clustering related meanings. The document provides examples of using related words and phrases for semantic topic clustering and ranking documents based on included phrases.

The document discusses how Apps Script can be used to program spreadsheets and leverage JavaScript functions and APIs. It provides examples of parsing URLs, cleaning data, and custom functions. Apps Script allows integrating APIs to scrape search results, classify data using machine learning, and monitor website changes. Functions can make spreadsheets more powerful and automate tasks like notifying users. The document encourages learning JavaScript and Apps Script to unlock these capabilities within spreadsheets.

Dan Taylor's #SEO lightning talk about incorporating entity analysis into your SEO strategy, and three methods of scaling it.

My slides from the Plerdy CRO/UX conference in February 2021, in which I discussed the relationship between SEO and UX, and how we can improve one to benefit the other.

Trying to scale your SEO strategies but having trouble keeping up? Is the rapid change in customer needs, churn rates, and product portfolios challenging your marketing team? Discover how you can overcome growing pains in our upcoming educational webinar specifically designed for enterprise marketers. In this presentation, you’ll learn: -How to use Edge SEO to automate and improve processes in product management (from an SEO perspective). -The best way to use entities scalably for better support of content creation. -How to deal with out-of-stock products to maintain brand visibility and avoid negatively impacting the user experience. -Often, enterprise ecommerce sites and websites that offer SaaS subscription models are challenged with automation and require technical assistance as they scale. Dan Taylor, Head Of Research And Development at SALT.agency – and a Search Engine Journal VIP Contributor – will discuss SEO strategies you should know to scale your efforts and grow your business.

Google uses over 200 factors when determining how to rank pages in its search results. These include on-page factors like keywords in page titles and content, as well as off-page factors like links pointing to the page. Some factors focus on usability and quality like page loading speed, mobile friendliness, and proper grammar. Historical factors like the age of the domain and how frequently the page has been updated are also considered. The exact role and importance of each individual factor is not fully known or static, as Google's algorithm is constantly evolving.

The document discusses issues with how computer science has directed the development of search systems, focusing on efficiency over user experience. It argues search systems have paid minimal attention to the user experience beyond results relevance and ad-matching. The goal of the plenary is to inspire designing search experiences that do more than just sell products well.

Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.

This document summarizes Amanda King's presentation on the new content SEO at the Sydney SEO Conference. It discusses how Google has moved beyond keywords and now understands content semantically through natural language processing and systems like BERT. It also explains how Google analyzes content through parsing, entity detection, and understanding relationships to score and rank pages. The presentation recommends doing a full content inventory to identify entities, related terms, and differences from top ranking pages to update content accordingly.