This document summarizes a presentation on web page classification techniques. It discusses the significance of web page classification and various applications such as constructing web directories, improving search results, and question answering systems. It then reviews common features used for classification, including on-page features like text, tags, and visual analysis, as well as neighbors features. Finally, it outlines different algorithms and approaches for classification, such as dimension reduction, relational learning methods, modifications to kNN and SVM algorithms, hierarchical classification, and combining multiple information sources.
The document discusses the inner workings of the Google search engine. It begins with facts about Google's founding and history. It then explains the basic components of how any search engine works, including web crawlers that index pages, and how keywords are matched to search results. The bulk of the document focuses on Google's specific architecture, including its web crawler called Googlebot, its indexer that catalogs words in a database, and its query processor that matches searches to relevant pages based on factors like PageRank. It also discusses related topics like search engine optimization techniques and using "Google digging" to refine searches.
Technical SEO refers to website optimizations that help search engines index a site effectively to improve organic rankings. This document provides an 8-step checklist for technical SEO best practices including using SSL, having a mobile-friendly responsive design, optimizing page speed, fixing duplicate content, creating an XML sitemap, enabling AMP, adding structured data markup, and registering the site with search console and webmaster tools. Following these guidelines can help ensure a site meets search engine expectations and is rewarded in search results.
AI-powered Semantic SEO by Koray GUBURAnton Shulke
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
The document discusses search engines and web crawlers. It provides information on how search engines work by using web crawlers to index web pages and then return relevant results when users search. It also compares major search engines like Google, Yahoo, MSN, Ask Jeeves, and Live Search based on factors like market share, database size and freshness, ranking algorithms, and treatment of spam. Google is highlighted as having the largest market share and best algorithms for determining natural vs artificial links.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
This document discusses how search engines work and provides tips for effective searching. It explains that search engines like Google use web crawlers to index web pages and search the content, tags, and links to provide relevant results. It outlines the major search engines and some alternative engines. The document also provides examples of advanced search techniques on Google, such as searching for exact phrases, excluding words, and searching within websites or file types. Finally, it discusses when not to use search engines and provides tips for managing information found online.
Bill Slawski SEO and the New Search ResultsBill Slawski
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 Python Cheat Sheet for the Busy MarketerHamlet Batista
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.
The document discusses the inner workings of the Google search engine. It begins with facts about Google's founding and history. It then explains the basic components of how any search engine works, including web crawlers that index pages, and how keywords are matched to search results. The bulk of the document focuses on Google's specific architecture, including its web crawler called Googlebot, its indexer that catalogs words in a database, and its query processor that matches searches to relevant pages based on factors like PageRank. It also discusses related topics like search engine optimization techniques and using "Google digging" to refine searches.
Technical SEO refers to website optimizations that help search engines index a site effectively to improve organic rankings. This document provides an 8-step checklist for technical SEO best practices including using SSL, having a mobile-friendly responsive design, optimizing page speed, fixing duplicate content, creating an XML sitemap, enabling AMP, adding structured data markup, and registering the site with search console and webmaster tools. Following these guidelines can help ensure a site meets search engine expectations and is rewarded in search results.
AI-powered Semantic SEO by Koray GUBURAnton Shulke
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
The document discusses search engines and web crawlers. It provides information on how search engines work by using web crawlers to index web pages and then return relevant results when users search. It also compares major search engines like Google, Yahoo, MSN, Ask Jeeves, and Live Search based on factors like market share, database size and freshness, ranking algorithms, and treatment of spam. Google is highlighted as having the largest market share and best algorithms for determining natural vs artificial links.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
This document discusses how search engines work and provides tips for effective searching. It explains that search engines like Google use web crawlers to index web pages and search the content, tags, and links to provide relevant results. It outlines the major search engines and some alternative engines. The document also provides examples of advanced search techniques on Google, such as searching for exact phrases, excluding words, and searching within websites or file types. Finally, it discusses when not to use search engines and provides tips for managing information found online.
Bill Slawski SEO and the New Search ResultsBill Slawski
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 Python Cheat Sheet for the Busy MarketerHamlet Batista
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 Lighthouse is super valuable but it only checks one page at a time.
Hamlet will show you how to get it to check all pages of a site, and how to run automated Lighthouse checks on-demand at scheduled intervals and from automated tests.
He'll also cover how to set performance budgets, how to get alerts when budgets are exceeded, and how to aggregate page reports using BigQuery and Google Data Studio.
The document introduces the PageRank algorithm. It explains that PageRank ranks pages based on the principle that pages with more inlinks are considered more important. It discusses how PageRank addresses issues like cyclic links and pages with no outlinks. It also provides the formula used to calculate PageRank through an iterative process and describes how Google uses over 200 factors beyond PageRank alone to determine search rankings.
Data Driven Approach to Scale SEO at BrightonSEO 2023Nitin Manchanda
With the help of my favourite case study, I'm showcasing how I took a data-driven approach to scale SEO for a travel brand.
I've covered how I collected data, found trends, and converted them into opportunities. Those opportunities were tested before the grand deployment, which resulted in multifold growth in SEO visibility and revenue.
Mobile First to AI First: How User Signals Change SEO | SMX19Philipp Klöckner
Traditional ranking factors have been great proxy metrics for the past years and have made Google the best search engine in the world. But as Google advances to an AI first company, SEOs have to change their metrics and toolsets as well. Learn about how Machine Learning changes SEO and why User Signals and UX have to guide your work.
This seminar presentation discusses search engines. It defines a search engine as a program that uses keywords to search documents and returns results in order of relevance. The presentation outlines the main components of a search engine: the web crawler, database, and search interface. It also describes how search engines work by crawling links, indexing words, and ranking pages using algorithms like PageRank. Finally, it discusses different types of search engines and how artificial intelligence is used to improve search engine quality.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Semantic seo and the evolution of queriesBill Slawski
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.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
Search engine optimization - SEO is the process of improving the volume or quality of traffic to your web site from search engines such as Google, Yahoo, MSN etc. via "organic" or un-paid search results.
Semantic search Bill Slawski DEEP SEA ConBill Slawski
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.
Technical SEO refers to optimizing a website for search engines by improving crawlability, speed, and other technical factors. Crawlability ensures search engines can access all pages through the robots.txt file. Speed is important for users and search engines; pages should load within 3 seconds. Other technical SEO factors include minimizing 404 errors, implementing HTTPS and SSL certificates, mobile-friendly design, XML sitemaps, and using Google Search Console and Bing Webmaster Tools to monitor performance. Technical SEO lays the foundation for effective marketing through search engines.
The document discusses SEO strategies and opportunities for 2022. It recommends focusing on content that solves problems for the target audience by identifying their problems and building content to address them better than competitors. Technical SEO should be optimized, including passage ranking, featured snippets, the EAT principle, and long-tail keywords. Both new and old content should be created and updated, and images, backlinks, and user experience should not be ignored. Brand authority and speed are also important soft factors.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
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.
This document discusses different types of search engines. It describes web-based search engines which search the internet, and system-based search engines which search files on a user's computer. The main types of web-based search engines discussed are crawler-based engines like Google which use bots to index webpages, directory-based engines like Yahoo which use human editors to categorize sites, and hybrid engines that combine both approaches. Other types discussed include meta search engines, paid inclusion, and specialty search engines for specific topics.
SEO, Search Engine Ranking Position (SERP) ReportKevin James
This document provides an SEO report and proposal for ServiceMaster Corporation. It analyzes the client's current website rankings, keywords, backlinks, and opportunities for improvement. The report finds the client's SEO grade is currently poor. It then proposes SEO services to implement like link building, on-page optimization, and keyword research to improve the client's organic search visibility and rankings over time. The proposal includes a breakdown of tasks and monthly costs to perform the recommended SEO work.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
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.
Technical SEO refers to optimizing a website for search engines by improving the structure, markup, and coding of web pages. This includes optimizing page speed, implementing a responsive design, creating an XML sitemap, fixing broken links, and using proper meta tags, headers, and canonical tags to avoid duplicate content issues. The goal of technical SEO is to make the website easy for search engines to crawl, index, and understand so content can be found and ranked highly.
This document provides an overview of search engines, including what they are, how they work, and the evolution of major search engines over time. It discusses how search engines use web crawlers to index web pages and how they developed ranking algorithms to return relevant results. Key points include:
- Search engines allow users to find information on the internet through keyword searches. They index web pages using crawlers and return ranked results based on relevance and popularity.
- Major early search engines included AltaVista, Yahoo, Ask Jeeves, and others. Google revolutionized search in 1998 with its PageRank algorithm that analyzed backlinks.
- Search engine algorithms consider many on-page and off-page
An introduction to Search Engine Optimization (SEO) and web analytics on fao.orgFAO
This document provides an introduction to search engine optimization (SEO) and web analytics. It outlines the objectives of optimizing web pages for visitors and search engines. The document explains how to apply basic SEO concepts like keywords, page titles, descriptions and links. It also discusses how to analyze user behavior and monitor key metrics using Google Analytics. Site visitors and search engine crawlers are able to find and index optimized pages. The document aims to teach the audience how to improve their website's visibility and understand user interactions.
Defining Content Categorization for a WebsiteJason Levine
The document outlines a plan to improve the usability, focus, and structure of a website by:
1) Defining user categories and focusing content on their needs
2) Simplifying the site architecture and navigation
3) Increasing usage and strengthening the brand image
This document discusses web page classification using naive Bayes classifiers. It outlines the goals of web page classification, including improving web directories and search results. The document reviews literature on different representations for classification, including bags of words, n-grams, using HTML structure, and visual analysis. It then describes experiments using a university web page dataset to classify pages into categories like course, department, etc. using bag of words, HTML tag weighting, and n-grams. The document concludes with an overview of evaluation techniques like k-fold cross validation and confusion matrices.
Google Lighthouse is super valuable but it only checks one page at a time.
Hamlet will show you how to get it to check all pages of a site, and how to run automated Lighthouse checks on-demand at scheduled intervals and from automated tests.
He'll also cover how to set performance budgets, how to get alerts when budgets are exceeded, and how to aggregate page reports using BigQuery and Google Data Studio.
The document introduces the PageRank algorithm. It explains that PageRank ranks pages based on the principle that pages with more inlinks are considered more important. It discusses how PageRank addresses issues like cyclic links and pages with no outlinks. It also provides the formula used to calculate PageRank through an iterative process and describes how Google uses over 200 factors beyond PageRank alone to determine search rankings.
Data Driven Approach to Scale SEO at BrightonSEO 2023Nitin Manchanda
With the help of my favourite case study, I'm showcasing how I took a data-driven approach to scale SEO for a travel brand.
I've covered how I collected data, found trends, and converted them into opportunities. Those opportunities were tested before the grand deployment, which resulted in multifold growth in SEO visibility and revenue.
Mobile First to AI First: How User Signals Change SEO | SMX19Philipp Klöckner
Traditional ranking factors have been great proxy metrics for the past years and have made Google the best search engine in the world. But as Google advances to an AI first company, SEOs have to change their metrics and toolsets as well. Learn about how Machine Learning changes SEO and why User Signals and UX have to guide your work.
This seminar presentation discusses search engines. It defines a search engine as a program that uses keywords to search documents and returns results in order of relevance. The presentation outlines the main components of a search engine: the web crawler, database, and search interface. It also describes how search engines work by crawling links, indexing words, and ranking pages using algorithms like PageRank. Finally, it discusses different types of search engines and how artificial intelligence is used to improve search engine quality.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Semantic seo and the evolution of queriesBill Slawski
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.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
Search engine optimization - SEO is the process of improving the volume or quality of traffic to your web site from search engines such as Google, Yahoo, MSN etc. via "organic" or un-paid search results.
Semantic search Bill Slawski DEEP SEA ConBill Slawski
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.
Technical SEO refers to optimizing a website for search engines by improving crawlability, speed, and other technical factors. Crawlability ensures search engines can access all pages through the robots.txt file. Speed is important for users and search engines; pages should load within 3 seconds. Other technical SEO factors include minimizing 404 errors, implementing HTTPS and SSL certificates, mobile-friendly design, XML sitemaps, and using Google Search Console and Bing Webmaster Tools to monitor performance. Technical SEO lays the foundation for effective marketing through search engines.
The document discusses SEO strategies and opportunities for 2022. It recommends focusing on content that solves problems for the target audience by identifying their problems and building content to address them better than competitors. Technical SEO should be optimized, including passage ranking, featured snippets, the EAT principle, and long-tail keywords. Both new and old content should be created and updated, and images, backlinks, and user experience should not be ignored. Brand authority and speed are also important soft factors.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
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.
This document discusses different types of search engines. It describes web-based search engines which search the internet, and system-based search engines which search files on a user's computer. The main types of web-based search engines discussed are crawler-based engines like Google which use bots to index webpages, directory-based engines like Yahoo which use human editors to categorize sites, and hybrid engines that combine both approaches. Other types discussed include meta search engines, paid inclusion, and specialty search engines for specific topics.
SEO, Search Engine Ranking Position (SERP) ReportKevin James
This document provides an SEO report and proposal for ServiceMaster Corporation. It analyzes the client's current website rankings, keywords, backlinks, and opportunities for improvement. The report finds the client's SEO grade is currently poor. It then proposes SEO services to implement like link building, on-page optimization, and keyword research to improve the client's organic search visibility and rankings over time. The proposal includes a breakdown of tasks and monthly costs to perform the recommended SEO work.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
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.
Technical SEO refers to optimizing a website for search engines by improving the structure, markup, and coding of web pages. This includes optimizing page speed, implementing a responsive design, creating an XML sitemap, fixing broken links, and using proper meta tags, headers, and canonical tags to avoid duplicate content issues. The goal of technical SEO is to make the website easy for search engines to crawl, index, and understand so content can be found and ranked highly.
This document provides an overview of search engines, including what they are, how they work, and the evolution of major search engines over time. It discusses how search engines use web crawlers to index web pages and how they developed ranking algorithms to return relevant results. Key points include:
- Search engines allow users to find information on the internet through keyword searches. They index web pages using crawlers and return ranked results based on relevance and popularity.
- Major early search engines included AltaVista, Yahoo, Ask Jeeves, and others. Google revolutionized search in 1998 with its PageRank algorithm that analyzed backlinks.
- Search engine algorithms consider many on-page and off-page
An introduction to Search Engine Optimization (SEO) and web analytics on fao.orgFAO
This document provides an introduction to search engine optimization (SEO) and web analytics. It outlines the objectives of optimizing web pages for visitors and search engines. The document explains how to apply basic SEO concepts like keywords, page titles, descriptions and links. It also discusses how to analyze user behavior and monitor key metrics using Google Analytics. Site visitors and search engine crawlers are able to find and index optimized pages. The document aims to teach the audience how to improve their website's visibility and understand user interactions.
Defining Content Categorization for a WebsiteJason Levine
The document outlines a plan to improve the usability, focus, and structure of a website by:
1) Defining user categories and focusing content on their needs
2) Simplifying the site architecture and navigation
3) Increasing usage and strengthening the brand image
This document discusses web page classification using naive Bayes classifiers. It outlines the goals of web page classification, including improving web directories and search results. The document reviews literature on different representations for classification, including bags of words, n-grams, using HTML structure, and visual analysis. It then describes experiments using a university web page dataset to classify pages into categories like course, department, etc. using bag of words, HTML tag weighting, and n-grams. The document concludes with an overview of evaluation techniques like k-fold cross validation and confusion matrices.
What is responsive typography all about? Why does reading distance matter? And how do you consider performance when thinking about typography on the web? All this and more, delivered at Web Directions Respond in 2015.
Web page classification features and algorithmsunyil96
This document summarizes research on classifying web pages. It discusses how web page classification is important for tasks like maintaining web directories, improving search results, and building focused crawlers. The document reviews different types of web page classification problems and features that are useful for classification, like content-based features and link-based features. It also discusses algorithms that have been used for web page classification.
This document provides an overview of how to get started with the SimilarWeb API. It discusses signing up for a free trial account, making API requests, and monitoring usage. The API allows users to retrieve web traffic and engagement data, mobile app details, and related websites for a given domain. Requests require a domain, endpoint, format, and user key. Time granularity and date ranges can also be specified for some endpoints. Usage is monitored and quotas tracked to avoid going over limits.
Text Categorization Using Improved K Nearest Neighbor AlgorithmIJTET Journal
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
This document discusses computer viruses including their history, symptoms, risky file types, and types of viruses. It provides details on some of the earliest computer viruses from the 1980s and 1990s. It describes common symptoms of a virus-infected computer like slow performance, crashes, and missing files. Risky file types that could contain viruses are listed as .exe, .pif, .bat, .vbs, and .com files. The different types of viruses are outlined such as file infector viruses, macro viruses, and polymorphic viruses. Examples are provided for some virus types like boot sector viruses and web scripting viruses.
This document provides an overview of computer viruses and methods for prevention and removal. It defines what a computer virus is and describes different types including boot sector viruses, file infector viruses, worms, macro viruses, and Trojan horses. Examples like Melissa and I Love You are given. Methods for manually removing some common viruses like New Folder.exe and Autorun.inf are outlined. The document stresses the importance of using antivirus software, regularly updating definitions, avoiding unsafe downloads and attachments, and making backups to help prevent and cure computer viruses.
1. The document describes how the author's experience with Emacs as a student taught him about software freedom and how to read and modify source code. This led him to create his own Emacs-based tools and influenced the design of Ruby.
2. Emacs taught the author the power of Lisp and how to implement a programming language and garbage collection. Using Emacs to write code, documents and email made him a more effective programmer.
3. Emacs had a profound influence on the author and changed his life by helping him become a hacker and free software advocate.
Technical SEO Myths Facts And Theories On Crawl Budget And The Importance Of ...Dawn Anderson MSc DigM
There are a lot of myths, facts and theories on crawl budget and the term is bandied around a lot. This deck looks to address some of those myths and also looks at some additional theories around the concepts of 'crawl rank' and 'search engine embarrassment'.
The document discusses web design and markup languages like HTML and XML. It provides an introduction to web design and why it is important, covering topics like first impressions, professionalism, and competition. The document then covers HTML and XML in more detail, including their structures and tags. It provides examples of basic HTML and XML code.
This presentation is a basic insight into the Application Layer Protocols i.e. Http & Https. I was asked to do this as a part of an interview round in one of the networking company.
-Kudos
Harshad Taware
Bangalore ,India
The document provides an overview of the Internet and IP addresses. It explains that the Internet is a worldwide collection of networks that connects millions of users. An IP address is a unique number assigned to devices connected to the Internet and is used to identify and locate the device. Domain names are easier for users to remember and are mapped to IP addresses by DNS servers. The document also discusses Internet service providers, types of Internet connections like broadband and dial-up, and the differences between IPv4 and IPv6 addressing.
This document discusses various internet services including email, instant messaging, the World Wide Web, voice over internet protocol (VoIP), message boards, file transfer protocol (FTP), and newsgroups. It defines each service and provides examples. E-mail is described as the transmission of messages and files via a computer network. Mailing lists and newsletters are discussed as ways to reach targeted audiences. Instant messaging and VoIP allow users to communicate in real-time over the internet. Message boards and newsgroups provide online spaces for discussion on particular topics. FTP is defined as a protocol that allows users to upload and download files between computers on the internet. Examples of FTP programs that can be used are provided.
Web browsers act as an interface between users and web servers by allowing users to locate and display web pages. Major features of web browsers include allowing users to open multiple pages simultaneously, refreshing pages, and including pop-up blockers. Browsers are made up of a user interface and rendering engine. Some of the earliest and most popular browsers include WorldWideWeb, Mosaic, Netscape Navigator, Internet Explorer, Firefox, Safari, Chrome, and browsers designed for mobile devices.
This document discusses various input and output devices used with computers. It describes common input devices like the mouse, keyboard, joystick, scanner, and barcode reader which are used to enter data and instructions into a computer. It then explains key output devices such as computer monitors, printers in different types like dot matrix, inkjet and laser, plotters which produce drawings, and microfilm/microfiche which store large amounts of data on film.
Computer viruses are small programs that spread from computer to computer and interfere with operations. They are deliberately created by programmers for reasons like research, pranks, attacks, or financial gain. Viruses typically spread through email attachments, downloads, or infected files on removable drives. Symptoms of infection include slow performance, file changes or damage. People can protect computers by only opening trusted email attachments, backing up files, scanning downloads, and using antivirus software.
This document discusses computer viruses including their similarities to biological viruses, how they work and spread, types of viruses, virus detection methods, and prevention. It notes that computer viruses can replicate and spread like biological viruses, infecting host systems and slowing them down. The main types discussed are macro, boot sector, worm, Trojan horse, and logic bomb viruses. Virus detection methods covered include signature-based, behavior-based, and heuristic-based detection. Prevention methods recommended are using antivirus software, not sharing drives without passwords, deleting email attachments, backing up files, and using secure operating systems.
The lies we tell our code, LinuxCon/CloudOpen 2015-08-18Casey Bisson
As presented at LinuxCon/CloudOpen 2015: http://sched.co/3Y3v
We tell our code lies from development to deploy. The most common of these lies start with the simple act of launching a virtual machine. These lies are critical to our applications. Some of them protect applications from themselves and each other, some even improve performance. Some, however, decrease performance, and others create barriers to simply getting things done.
We lie about the systems, networks, storage, RAM, CPU and other resources our applications use, but how we tell those lies is critical to how the applications that depend on them perform. Joyent's Casey Bisson will explore the lies we tell our code and demonstrate examples of how they sometimes help and hurt us.
positive examples are used to SVM classifier
examples from train initial SVM with positive
labeled data classifier examples
4th: Classifier labels 5th: Unlabeled data is 6th: Labeled as
unlabeled data labeled based on negative if not
7th: Labeled data classifier's prediction predicted as
augments the 8th: New classifier is positive
positive examples retrained with 7th: Process repeats
augmented data
This document summarizes a conference paper on using machine learning algorithms for static page ranking. It discusses how supervised learning algorithms like RankNet can be used to combine multiple static features, like page content, links, and popularity data, to generate page rankings. The paper finds this machine learning approach significantly outperforms traditional PageRank, providing more robust and less technology-biased rankings. Features, ranking methods, applications, and algorithms are described in detail. The conclusions recommend further experimentation with additional features and machine learning techniques to improve static page ranking.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
School of Internet Marketing is the best training institute for digital marketing in Pune as they provide theory and practical knowledge to their students.
PageRank algorithm and its variations: A Survey reportIOSR Journals
This document provides an overview and comparison of PageRank algorithms. It begins with a brief history of PageRank, developed by Larry Page and Sergey Brin as part of the Google search engine. It then discusses variants like Weighted PageRank and PageRank based on Visits of Links (VOL), which incorporate additional factors like link popularity and user visit data. The document also gives a basic introduction to web mining concepts and categorizes web mining into content, structure, and usage types. It concludes with a comparison of the original PageRank algorithm and its variations.
What is the current status quo of the Semantic Web as first mentioned by Tim Berners Lee in 2001?
Not only 10 blue links can drive you traffic anymore, Google has added many so called Knowlegde cards and panels to answer the specific informational need of their users. Sounds complicated, but it isn’t. If you ask for information, Google will try to answer it within the result pages.
I'll share my research from a theoretical point of view through exploring patents and papers, and actual testing cases in the live indices of Google. Getting your site listed as the source of an Answer Card can result in an increase of CTR as much as 16%. How to get listed? Come join my session and I'll shine some light on the factors that come into play when optimizing for Google's Knowledge graph.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
IRJET-Multi -Stage Smart Deep Web Crawling Systems: A ReviewIRJET Journal
This document summarizes research on multi-stage smart deep web crawling systems. It discusses challenges in efficiently locating deep web interfaces due to their large numbers and dynamic nature. It proposes a three-stage crawling framework to address these challenges. The first stage performs site-based searching to prioritize relevant sites. The second stage explores sites to efficiently search for forms. An adaptive learning algorithm selects features and constructs link rankers to prioritize relevant links for fast searching. Evaluation on real web data showed the framework achieves substantially higher harvest rates than existing approaches.
This document discusses how academics can leverage their existing academic publications and research to establish an online presence through search engine optimization. It notes that academics already produce large volumes of well-written, keyword-rich text through their research and publishing activities. This body of work represents a valuable resource that can be used to create web content and populate various online platforms. The document outlines techniques for hosting academic content online, submitting sites to search engines, and monitoring website visibility over time to improve search engine rankings. It argues that with some SEO efforts, academics can promote their research topics and expertise online without incurring significant costs.
Search engines crawl the web by following links between pages to index their content. They have two main functions: crawling and indexing billions of webpages to build a database, and providing search results by ranking pages in order of relevance to user queries. SEO techniques help pages rank higher through on-page optimization and link building. While search engines are sophisticated, they have limitations understanding certain types of content like forms, duplicate pages, and language variations, which is where SEO helps guide them.
This document discusses several key aspects of mathematics and algorithms used in internet information retrieval and search engines:
1. It explains how search engines like Google can rapidly rank billions of web pages using algorithms based on the topology and link structure of the web graph, such as PageRank.
2. It describes two main types of page ranking algorithms - static importance ranking based on link analysis, and dynamic relevance ranking based on statistical learning models to match pages to queries.
3. It proposes a new ranking algorithm called BrowseRank that models user browsing behavior using Markov chains and takes into account visit duration to better reflect true page importance.
The document discusses several mathematical models and algorithms used in internet information retrieval and search engines:
1. Markov chain methods can be used to model a user's web surfing behavior and page visit transitions.
2. BrowseRank models user browsing as a Markov process to calculate page importance based on observed user behavior rather than artificial assumptions.
3. Learning to rank problems in information retrieval can be framed as a two-layer statistical learning problem where queries are the first layer and document relevance judgments are the second layer.
4. Stability theory can provide generalization bounds for learning to rank algorithms under this two-layer framework. Modifying algorithms like SVM and Boosting to have query-level stability improves performance.
Team of Rivals: UX, SEO, Content & Dev UXDC 2015Marianne Sweeny
The search engine landscape has changed dramatically and now relies heavily on user experience signals to influence rank in search results. In this presentation, I explore search engine methods for evaluating UX in a machine readable fashion and present a framework for successful cross-discipline collaboration.
The document summarizes how search engines work and what factors influence search engine rankings. It discusses:
1. Search engines crawl and index billions of webpages and files to build an index that allows them to provide fast answers to user search queries.
2. Hundreds of factors can influence search engine rankings, including the number of links to a page and the content and updates to pages.
3. Through experiments and testing variations in page elements like keywords, formatting, and link structures, search marketers have studied search engine algorithms to learn how to improve rankings.
This document discusses modern web search and Google's search engine architecture specifically. It describes how search engines work by crawling the web to create an index rather than searching the live web. It then details Google's crawling system, how it indexes pages by creating an inverted index, and how it ranks pages using factors like PageRank to determine relevance. The document provides technical details on Google's implementation and challenges in building large-scale search engines.
This document discusses modern web search and Google's search engine architecture specifically. It describes how search engines work by crawling the web to create an index rather than searching the live web. It then details Google's crawling system, how it indexes pages by creating an inverted index, and how it ranks pages using factors like PageRank to determine relevance. The document provides technical details on Google's systems for scalability and speeding up query processing.
Optimizing Library Websites for Better VisibilityErin Rushton
Binghamton University Librarians have attempted to employ search engine optimization strategies to make their website more visible on search engine result pages. Search engine optimization is the practice of improving ranking on search engine result pages and also increasing targeted traffic to a website. The presenter will discuss the effectiveness (or lack thereof) of developing a “do it yourself” optimization strategy for library websites
Optimizing Library Websites for Better VisibilityErin Rushton
Erin Rushton presented on her library's experiment with search engine optimization (SEO) to improve the visibility of their website and selected pages. They optimized pages like "Ask a Librarian" and pages on special collections. After optimization, they saw increased traffic to some pages from search engines and their internal search. Their goals are to continue optimizing their new website and share best practices with other university departments.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
1. Web Page Classification Feature and Algorithms XiaoguangQi and Brian D. Davison Department of Computer Science & Engineering Lehigh University, June 2007 Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
2. Agenda Webpage classification significance Introduction Background Applications of web classification Features Algorithms Blog Classification Conclusion
16. Webpage classification significance What’s different between past and present what changed? Flash animation Java Script Video Clips, Embedded Object Advertise, GG Ad sense, Yahoo!
17. Introduction Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
18. Introduction Webpage classification or webpage categorization is the process of assigning a webpage to one or more category labels. E.g. “News”, “Sport” , “Business” GOAL: They observe the existing of web classification techniques to find new area for research. Including web-specific features and algorithms that have been found to be useful for webpage classification.
19. Introduction What will you learn? A Detailed review of useful features for web classification The algorithms used The future research directions Webpage classification can help improve the quality of web search. Knowing is thing help you to improve your SEO skill. Each search engine, keep their technique in secret.
20. Background Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
21. Background The general problem of webpage classification can be divided into Subject classification; subject or topic of webpage e.g. “Adult”, “Sport”, “Business”. Function classification; the role that the webpage play e.g. “Personal homepage”, “Course page”, “Admission page”.
22. Background Based on the number of classes in webpage classification can be divided into binary classification multi-class classification Based on the number of classes that can be assigned to an instance, classification can be divided into single-label classification and multi-label classification.
24. Applications of web classification Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
25. Applications of web classification Constructing and expanding web directories (web hierarchies) Yahoo ! ODP or “Open Dictionary Project” http://www.dmoz.org How are they doing?
27. Applications of web classification How are they doing? By human effort July 2006, it was reported there are 73,354 editor in the dmoz ODP. As the web changes and continue to grow so “Automatic creation of classifiers from web corpora based on use-defined hierarchies” has been introduced by Huang et al. in 2004 The starting point of this presentation !!
28. Applications of web classification Improving quality of search results Categories view Ranking view
30. Applications of web classification Improving quality of search results Categories view Ranking view In 1998, Page and Brin developed the link-based ranking algorithm called PageRank Calculates the hyperlinks with our considering the topic of each page
32. Applications of web classification Helping question answering systems Yang and Chua 2004 suggest finding answers to list questions e.g. “name all the countries in Europe” How it worked? Formulated the queries and sent to search engines. Classified the results into four categories Collection pages (contain list of items) Topic pages (represent the answers instance) Relevant page (Supporting the answers instance) Irrelevant pages After that , topic pages are clustered, from which answers are extracted. Answering question system could benefit from web classification of both accuracy and efficiency
33. Applications of web classification Other applications Web content filtering Assisted web browsing Knowledge base construction
34. Features Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
35. Features In this section, we review the types of features that useful in webpage classification research. The most important criteria in webpage classification that make webpage classification different from plaintext classification is HYPERLINK <a>…</a> We classify features into On-page feature: Directly located on the page Neighbors feature: Found on the pages related to the page to be classified.
36. Features: On-page Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
37. Features: On-page Textual content and tags N-gram feature Imagine of two different documents. One contains phrase “New York”. The other contains the terms “New” and “York”. (2-gram feature). In Yahoo!, They used 5-grams feature. HTML tags or DOM Title, Headings, Metadata and Main text Assigned each of them an arbitrary weight. Now a day most of website using Nested list (<ul><li>) which really help in web page classification.
38. Features: On-page Textual content and tags URL Kan and Thi 2004 Demonstrated that a webpage can be classified based on its URL
39. Features: On-page Visual analysis Each webpage has two representations Text which represent in HTML The visual representation rendered by a web browser Most approaches focus on the text while ignoring the visual information which is useful as well Kovacevic et al. 2004 Each webpage is represented as a hierarchical “Visual adjacency multi graph.” In graph each node represents an HTML object and each edge represents the spatial relation in the visual representation.
41. Features: Neighbors Features Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
42. Features: Neighbors Features Motivation The useful features that we discuss previously, in a particular these features are missing or unrecognizable
44. Features: Neighbors features Underlying Assumptions When exploring the features of neighbors, some assumptions are implicitly made in existing work. The presence of many “sports” pages in the neighborhood of P-a increases the probability of P-a being in “Sport”. Chakrabari et al. 2002 and Meczer 2005 showed that linked pages were more likely to have terms in common . Neighbor selection Existing research mainly focuses on page with in two steps of the page to be classified. At the distance no greater than two. There are six types of neighboring pages: parent, child, sibling, spouse, grandparent and grandchild.
46. Features: Neighbors features Neighbor selection cont. Furnkranz 1999 The text on the parent pages surrounding the link is used to train a classifier instead of text on the target page. A Target page will be assigned multiple labels. These label are then combine by some voting scheme to form the final prediction of the target page’s class Sun et al. 2002 Using the text on the target page. Using page title and anchor text from parent pages can improve classification compared a pure text classifier.
47. Features: Neighbors features Neighbor selection cont. Summary Using parent, child, sibling and spouse pages are all useful in classification, siblings are found to be the best source. Using information from neighboring pages may introduce extra noise, should be use carefully.
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49. Features: Neighbors features Features Label : by editor or keyworder Partial content : anchor text, the surrounding text of anchor text, titles, headers Full content Among the three types of features, using the full content of neighboring pages is the most expensive however it generate better accuracy.
50. Features: Neighbors features Utilizing artificial links (implicit link) The hyperlinks are not the only one choice. What is implicit link? Connections between pages that appear in the results of the same query and are both clicked by users. Implicit link can help webpage classification as well as hyperlinks.
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52. Discussion: Features However, since the results of different approaches are based on different implementations and different datasets, making it difficult to compare their performance. Sibling page are even more use full than parents and children. This approach may lie in the process of hyperlink creation. But a page often acts as a bridge to connect its outgoing links, which are likely to have common topic.
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54. Tip!Tracking Incoming LinkHow to know when someone link to you? Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
55. Algorithms Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
58. Way of boosting the classification by emphasizing the features with the better discriminative power
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60. Dimension Reduction (con) : Feature Selection Simple approaches First fragment of each document First fragment to the web documents in hierarchical classification Text categorization approaches Information gain Mutual information Etc.
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62. Feature Selection (Cont’d): Text Categorization Measures Using expected mutual information and mutual information Two well-known metrics based on variation of the k-Nearest Neighbor algorithm Weighted terms according to its appearing HTML tags Terms within different tags handle different importance Using information gain Another well-known metric Still not apparently show which one is more superior for web classification
63. Feature Selection (Cont’d): Text Categorization Measures Approving the performance of SVM classifiers By aggressive feature selection Developed a measure with the ability to predict the selection effectiveness without training and testing classifiers A popular Latent Semantic Indexing (LSI) In Text documents: Docs are reinterpreted into a smaller transformed, but less intuitive space Cons:high computational complexity makes it inefficient to scale in Web classification Experiments based on small datasets (to avoid the above ‘cons’) Some work has approved to make it applicable for larger datasets which still needs further study
66. Relational Learning (cont’d): 2 Main Approaches Relaxation Labeling Algorithms Original proposal: Image analysis Current usage: Image and vision analysis Artificial Intelligence pattern recognition web-mining Link-based Classification Algorithms Utilizing 2 popular link-based algorithms Loopy belief propagation Iterative classification
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68. Relational Learning (cont’d): Link-based Classification Algorithms Two popular link-based algorithms: Loopy belief propagation Iterative classification Better performance on a web collection than textual classifiers During the scientists’ study, ‘a toolkit’ was implemented Toolkit features Classify the networked data which utilized a relational classifier and a collective inference procedure Demonstrated its great performance on several datasets including web collections
70. Modifications to traditional algorithms The traditional algorithms adjusted in the context of Webpage classification k-Nearest Neighbors (kNN) Quantify the distance between the test document and each training documents using “a dissimilarity measure” Cosine similarity or inner product is what used by most existing kNN classifiers Support Vector Machine (SVM)
71. Modification Algorithms (Cont’d) : k-Nearest Neighbors Algorithm Varieties of modifications: Using the term co-occurrence in document Using probability computation Using “co-training”
72. k-Nearest Neighbors Algorithm(Cont’d): Modification Varieties Using the term co-occurrence in documents An improved similarity measure The more co-occurred terms two documents have in common, the stronger the relationship between them Better performance over the normal kNN (cosine similarity and inner product measures) Using the probability computation Condition: The probability of a document d being in class c is determined by its distance b/w neighbors and itself and its neighbors’ probability of being in c Simple equation Prob. of d @ c = (distance b/w d and neighbors)(neighbors’ Prob. @ c)
73. k-Nearest Neighbors Algorithm(Cont’d): Modification Varieties (2) Using “Co-training” Make use of labeled and unlabeled data Aiming to achieve better accuracy Scenario: Binary classification Classifying the unlabeled instances Two classifiers trained on different sets of features The prediction of each one is used to train each other Classifying only labeled instances The co-training can cut the error rate by half When generalized to multi-class problems When the number of categories is large Co-training is not satisfying On the other hand, the method of combining error-correcting output coding (more than enough classifiers in use), with co-training can boost performance
74. Modification Algorithms (Cont’d) : SVM-based Approach In classification, both positive and negative examples are required SVM-Based aim: To eliminate the need for manual collection of negative examples while still retaining similar classification accuracy
76. Take a Break!The Internet’s Ad Market PlaceBesides Google Adwords Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
78. Hierarchical Classification Not so many research since most web classifications focus on the same level approaches Approaches: Based on “divide and conquer” Error minimization Topical Hierarchy Hierarchical SVMs Using the degree of misclassification Hierarchical text categoriations
79. Hierarchical Classification (Cont’d): Approaches The use of hierarchical classification based on “divide and conquer” Classification problems are splitted into sub-problems hierarchically More efficient and accurate that the non-hierarchical way Error minimization when the lower level category is uncertain, Minimize by shifting the assignment into the higher one Topical Hierarchy Classify a web page into a topical hierarchy Update the category information as the hierarchy expands
80. Hierarchical Classification (Cont’d): Approaches (2) Hierarchical SVMs Observation: Hierarchical SVMs are more efficient than flat SVMs None are satisfying the effectiveness for the large taxonomies Hierarchical settings do more harm than good to kNNs and naive Bayes classifiers Hierarchical Classification By the degree of misclassification Opposed to measuring “correctness” Distance are measured b/w the classifier-assigned classes and the true class. Hierarchical text categorization A detailed review was provided in 2005
82. Combining Information from Multiple Sources Different sources are utilized Combining link and content information is quite popular Common combination way: Treat information from ‘different sources’ as ‘different (usually disjoint) feature sets’ on which multiple classifiers are trained Then, the generation of FINAL decision will be made by the classifiers Mostly has the potential to have better knowledge than any single method
83. Information Combination (Cont’d): Approaches Voting and Stacking The well-developed method in machine learning Co-Training Effective in combining multiple sources Since here, different classifiers are trained on disjoint feature sets
84. Information Combination (Cont’d): Cautions Please be noted that: Additional resource needs sometimes cause ‘disadvantage’ The combination of 2 does NOT always BETTER than each separately
85. Blog classification Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
86. Take a Break!Follow the Trend!!Everybody RETWEET!! Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
87. Follow me on TwitterFollow pChralso my Blog Http://www.PacharaStudio.com Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
88. Blog classification The word “blog” was originally a short form of “web log” Blogging has gained in popularity in recent years, an increasing amount of research about blog has also been conducted. Broken into three types Blog identification (to determine whether a web document is a blog) Mood classification Genre classification
89. Blog classification Elgersma and Rijke 2006 Common classification algorithm on Blog identification using number of human-selected feature e.g. “Comments” and “Archives” Accuracy around 90% Mihalcea and Liu 2006 classify Blog into two polarities of moods, happiness and sadness (Mood classification) Nowson 2006 discussed the distinction of three types of blogs (Genre Classification) News Commentary Journal
90. Blog classification Qu et al. 2006 Automatic classification of blogs into four genres Personal diary New Political Sports Using unigram tfidf document representation and naive Bayes classification. Qu et al.’s approach can achieve an accuracy of 84%.
91. Conclusion Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
92. Conclusion Webpage classification is a type of supervised learning problem that aims to categorize webpage into a set of predefined categories based on labeled training data. They expect that future web classification efforts will certainly combine content and link information in some form.
93. Conclusion Future work would be well-advised to Emphasize text and labels from siblings over other types of neighbors. Incorporate anchor text from parents. Utilize other source of (implicit or explicit) human knowledge, such as query logs and click-through behavior, in addition to existing labels to guide classifier creation.
94. Thank you. Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009
95. Question? Presented by Mr.Pachara Chutisawaeng Department of Computer Science Mahidol University, July 2009