Keyword Research and Topic Modeling in a Semantic Web

Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.

This document summarizes several patents related to query parsing and semantic search. It describes patents for multi-stage query processing, query breadth, query analysis, midpage query refinements (search suggestions), context vectors, and categorical quality (re-ranking search results based on the category of the query). Each patent is briefly described, including inventors, filing dates, and some technical details. The document aims to provide an overview of the evolution of semantic search and query understanding technologies at Google.

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

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 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.

A look at Google moving from indexing URLs to Indexing Data on the Web, and The Triggering of Direct Answers, Schema and Structured Snippets

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.

The document discusses using Python for SEO applications such as data extraction, preparation, analysis, machine learning and deep learning. It provides an agenda and examples of using Python to solve challenging SEO problems from site migrations and traffic losses. Methods demonstrated include pulling data from Google Analytics, storing in DataFrames, regular expression grouping, and training machine learning models on page features to classify page groups and identify losses. Later sections discuss using deep learning with computer vision models to classify web pages from screenshots.

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 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.

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

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.

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.

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.

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.

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.

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.

Having consulted and trained a myriad of companies, from start ups right up to some of the biggest social media properties in the world, Ned Poulter (Pole Star Digital) has amassed a total client ad spend on social in excess of £1M. He has harnesses a wealth of knowledge when it comes to strategising and implementing paid social media strategies. In this session Ned communicated his findings in a form of actionable takeaways, including insights on: Campaign strategy development, recommendations on tools to use for your Facebook ad campaigns, creative ways to developing target audiences and Insight into the latest Facebook marketing ad formats – and why you should be pushing your creative boundaries, using newer formats available.

Presented at Pubcon Las Vegas on November 9, 2017. Learn a different approach to local ranking factors and how to truly figure out what works for your site or your clients. With specific tips for local links and local content, learn how you can boost your site's local visibility in Google.

Local search ranking is determined by 41 signals and factors related to branding, domains, landing pages, hosting, content, keywords, links, citations, reviews, social media, conversion tracking, and consistency of business information. Key factors include technical on-page optimization, local link building, Google My Business optimization with complete information and frequent reviews, and social media engagement to build trust as a local expert. Ranking first requires addressing all these areas to satisfy searchers and earn clicks for ads.

Slide presentation from Kevin Doory Technical SEO to a local search campaign and simplifying the workflow of a technical SEO/content audit.

This document discusses semantic markup basics and recent developments. It covers: - Semantic markup is getting more complex as new types are introduced and implementations require more details. - The latest schema.org release added a HowTo type and reworked some properties. - Upcoming changes may include updates to occupation, education, and employer markup. - The document provides tips on implementing semantic markup and engaging with schema.org developments.

Malcolm’s presentation, Brand: The Only Future Ranking Factor, revisits research he carried out back in 2012 to see if he was right about the importance of brand signals in SEO and the influence brand power has in affecting rankings. Additionally, Malcolm also discussed what the state of play for SEO looks like in 2017, and the potential opportunities that will drive search success in the near future.

This document provides a summary of Michael King's presentation on the technical SEO renaissance. It discusses how SEO has evolved over time from basic tricks to a more technical focus as search engines have advanced. Key points include the growing importance of JavaScript, single page applications, HTTP headers, log file analysis, headless browsing, scraping techniques, content optimization using entities, internal linking structures, page speed optimizations, and preloading directives. The presentation argues that technical skills are now essential for SEOs to understand new developments and effectively optimize websites.

This document discusses how to optimize content for the future of search marketing with a focus on voice search. It notes that consumers are increasingly asking questions through voice and expecting personalization. To prepare, marketers need to conduct keyword research on long-tail question-based keywords, create content that answers questions directly and ranks for featured snippets, use schema markup to provide structured data to search engines, and optimize their overall online brand presence through various marketing channels. Foundational steps include defining goals, understanding audiences, and mapping the customer journey.

- More than half of the world's population now uses the internet, with global internet users growing 8% year-over-year. Mobile internet and social media usage are also growing significantly. - Social media users grew over 20% in the past year to over 2.5 billion active users monthly. Mobile social media use in particular saw 30% growth. - The report provides statistics on internet, social media, and mobile usage globally and by region, finding continued growth in connectivity and usage around the world.

The document discusses the history and development of Google's search technology. It describes how Google founders Larry Page and Sergey Brin met at Stanford University and collaborated on early search projects. It then outlines key milestones in Google's search capabilities, including the development of PageRank, knowledge graphs, and using contextual information to better understand user queries.