AI-powered Semantic SEO by Koray GUBUR

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.

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

What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.

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.

The document describes a Python script that can automatically generate new subcategories for an ecommerce website based on clustering product names. It discusses: - Using NLTK to generate n-grams from product names to cluster related products - Filtering the n-grams to keep only those with commercial value by checking for search volume and CPC data - Running the script on a large home improvement site to identify over 1,650 new subcategory opportunities with a total search volume of over 13 million - Sharing the script so others can automate subcategory identification for their own sites to scale up an important SEO tactic.

This document provides SEO metrics and comparisons for the website hangikredi.com over several time periods between April 2019 and September 2019. It shows substantial increases in key metrics like organic traffic, clicks, impressions, and average position after Google algorithm updates in May, June, July, and September. However, it also shows significant drops in these metrics during a server outage in early August. Overall the data demonstrates the site's strong SEO performance and organic growth over the 6-month period analyzed.

Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.

1) Knowledge graphs are structured databases that represent real-world entities and their relationships to each other. They help search engines like Google understand topics at a deeper level. 2) Entities (topics) are becoming more important than keywords for search engines to understand content. Google's entity understanding can be checked using their natural language processing tool. 3) Semantic SEO techniques like tightly linking topics both internally and to relevant external pages can help improve how search engines understand and represent the entities within a website through their knowledge graphs.

Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively. It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.

My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.

This document discusses internal linking strategies and techniques. It begins by explaining the benefits of connecting entities within content, rather than just words, and translating those connections into internal links. It then provides an overview of technologies like PageRank, the reasonable surfer algorithm, topical PageRank, chunking, and natural language processing that search engines use to understand contexts and how those ideas can be applied to internal linking at scale. Specific options for approaches to internal linking existing pages are also outlined.

Google's search results now include entities and concepts. Entities refer to people, places, things, and 20-30% of queries are for name entities. Google uses meta data like Freebase to build a taxonomy of entities and their relationships. This supports features like the Knowledge Graph, which provides information panels, and allows querying of nearby entities which may soon be available in search results.

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.

A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015 The Lotico Semantic Web of San Diego The SEO San Diego Meetup The SEM San Diego Meetup http://www.meetup.com/InternetMarketingSanDiego/events/222788495/ User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route. This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.

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.

1) Google uses various techniques to extract structured information like entities, relationships, and properties from unstructured text on the web and databases. This extracted information is then used to generate knowledge graphs and provide augmented responses to user queries. 2) One key technique is to identify patterns in which tuples of information are stored in databases, and then extract additional tuples by repeating the process and utilizing the identified patterns. 3) Google also extracts entities from user queries and may generate a knowledge graph to answer questions by providing information about the entities from sources like its own knowledge graph and information extracted from the web.

This document provides an overview of entity SEO, including: - What an entity is and why entity SEO is important as search engines have evolved from information engines to knowledge engines - How search algorithms like Panda, Penguin, and Hummingbird helped drive this transition by prioritizing high-quality content over low-quality sites - Techniques for entity SEO including entity research, topical maps, schema, internal linking, and case studies - Tools like Google's Knowledge Graph that can help with entity research and understanding how entities are ranked

This document discusses Amazon's artificial intelligence services including Polly, Rekognition, and Lex. Polly provides text-to-speech conversion in multiple languages. Rekognition allows image analysis including object detection, facial detection and analysis. Lex builds conversational interactions through voice and text using natural language understanding. The document demonstrates how these services work through examples and emphasizes their ease of use, quality, functionality and integration capabilities. It positions Amazon Web Services as the center of gravity for artificial intelligence.