Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
There are three main components of information retrieval systems: query understanding, document-query relevance understanding, and document clustering and ranking. The path from a search query to a search document involves several steps like query parsing, processing, augmenting, scoring, ranking, and clustering. Query understanding is where search engine optimization (SEO) begins, while document creation and ranking are other areas where SEO is applied. Cranfield experiments in the late 1950s helped develop the concept of a "search query language" which is different from the language used in documents. Formal semantics and components like tense, aspect, and mood can help machines better understand human language for information retrieval tasks.
40 Deep #SEO Insights for 2023:
-In 2022, I told to focus on Natural Language Generation, and it happened.
-In 2023, F-O-C-U-S on "Information Density, Richness, and Unique Added Value" with Microsemantics.
I call the collection of these, "Information Responsiveness".
1/40 🧵.
1. PageRank Increases its Prominence for Weighting Sources
Reason: #AI and automation will bloat the web, and the real authority signals will come from PageRank, and Exogenous Factors.
The expert-like AI content and real expertise are differentiated with historical consistency.
2. Indexing and relevance thresholds will increase.
Reason: A bloated web creates the need for unique value to be added to the web with real-world expertise and organizational signals. The knowledge domain terms, or #PageRank, will be important in the future of a web source.
3. AI and #automation filters will be created.
Reason: Google needs to filter the websites that publish 500 articles a day on multiple topics to find non-expert websites. This is already happening.
4. #Google will start to make mistakes in filtering websites that use spam and AI.
Reason: The need for AI-generated content filtration forced Google to check and audit "momentum", in other words, content publication frequency.
I used the "momentum" first in TA Case Study.
5. Google uses #Author Vectors, and Author Recognition.
Reason: LLMs use certain types of language styles and word sequences by leaving a watermark behind them. It is easy to understand which websites do not use a real expert for their articles, and content to differentiate.
6. #Microsemantics will be the name of the next game.
Reason: The bloating on the web will create bigger web document clusters, and being a representative source will be more important.
Thus, micro-differences inside the content will create higher unique value.
7. Custom #LLMs will be rented.
Reason: Custom and unique LLMs will be trained and rented to the people who try to create 100 websites with 100,000 content items per website.
NLP in SEO will show its true monetary value in mid-2023.
8. Advanced Semantic SEO will be a must for every SEO.
Reason: 20 years of websites will lose their rankings to the new websites that come with 60,000 articles. This creates the need for advanced #Semantics and Lingusitics capabilities for SEOs.
9. Cost-of-retrieval will be a base concept for #SEO, as TA.
Reason: TA explains a big portion of how the web works. Information Responsiveness and Cost-of-retrieval will complete it further.
For two books, I will be publishing only these two concepts.
10. Google Keys
Reason: The biggest Google leak after Quality Rater Guidelines will happen in 2023. And, I will be involved, but no more information, for now, I am not allowed to share more.
Check the slides for the next SEO Insights for 2023.
#searchengineoptimization #future #nlp #semantic #chatgpt #ai #content #quality #publishing #trend #seotrend #seo #searchengineoptimisation
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.
William slawski-google-patents- how-do-they-influence-searchBill Slawski
Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.
Quality Content at Scale Through Automated Text Summarization of UGCHamlet Batista
The document discusses using automated text summarization techniques to generate quality content at scale from user-generated content like online product reviews. It proposes a technical plan to download Amazon reviews, remove duplicate sentences using neural semantic textual similarity, and then generate frequently asked questions and corresponding FAQ schema by feeding the review text into a neural question generation model. The goal is to leverage user content and machine learning to automatically create helpful content for websites.
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.
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.
Everything You Didn't Know About Entity SEO Sara Taher
This document provides an overview of entity SEO, including:
- What an entity is and why entity SEO is important as search engines have evolved from information engines to knowledge engines
- How search algorithms like Panda, Penguin, and Hummingbird helped drive this transition by prioritizing high-quality content over low-quality sites
- Techniques for entity SEO including entity research, topical maps, schema, internal linking, and case studies
- Tools like Google's Knowledge Graph that can help with entity research and understanding how entities are ranked
Cost Effective Multilingual Content Optimization in An International SEO ProcessAleyda Solís
How to optimize your content in an international / Multilingual SEO process? Take a look at the criteria to take into consideration and tips to maximize results.
SEO Tool Overload😱... Google Data Studio to the rescueNils De Moor
Google Sheet Template >>> http://bit.ly/seotooloverload-sheet
Ask any person in SEO what tool they use, and you'll more likely than not get a list of tools answered. SEO's need different perspectives, the right tool for the right job, but with an explosion of data produced by these tools, things get overwhelming really fast. To be able to tie things together, Nils will explore ways to streamline the data from your tools and build a single source of truth with Google Data Studio, helping you to make the right decisions.
You'll learn about using QUERY functions in Google Sheets, applying Machine Learning to do fuzzy matching on keywords and search queries, and much more...
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Want access to the Google Sheets and Google Data Studio TEMPLATES --> bit.ly/seotooloverload-sheet
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How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
No More "It Depends" - Learn to Set your Visual SEO Resources #LondonSEOMeetu...Aleyda Solís
This document discusses how SEOs often answer questions with the vague response of "it depends" and provides better alternatives. It recommends developing reusable resources like diagrams, charts and frameworks to more clearly explain SEO scenarios, processes and criteria. This helps avoid vague answers, establish trust, and facilitate decision making. It also encourages analyzing activities and setting replicable systems to improve services and grow expertise.
SEO Case Study - Hangikredi.com From 12 March to 24 September Core UpdateKoray Tugberk GUBUR
This document provides SEO metrics and comparisons for the website hangikredi.com over several time periods between April 2019 and September 2019. It shows substantial increases in key metrics like organic traffic, clicks, impressions, and average position after Google algorithm updates in May, June, July, and September. However, it also shows significant drops in these metrics during a server outage in early August. Overall the data demonstrates the site's strong SEO performance and organic growth over the 6-month period analyzed.
1) Knowledge graphs are structured databases that represent real-world entities and their relationships to each other. They help search engines like Google understand topics at a deeper level.
2) Entities (topics) are becoming more important than keywords for search engines to understand content. Google's entity understanding can be checked using their natural language processing tool.
3) Semantic SEO techniques like tightly linking topics both internally and to relevant external pages can help improve how search engines understand and represent the entities within a website through their knowledge graphs.
Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively.
It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.
7 E-Commerce SEO Mistakes & How to Fix Them #DeepSEOConAleyda Solís
Avoid the most common SEO issues, challenges and mistakes by going through this presentation with tips, criteria and tools to use independently of your online store Web platform, and grow your organic search results
Thank U (Rel) Next - State of Retail Pagination 1Y Later - Orit Mutznik - Bri...Orit Mutznik
BrightonSEO April 2019 - I attended Adam Gent's presentation, in which he presented pagination best practices given Google's recent announcement of rel next/prev deprecation. After not being sure about what to do with our pagination, this talk came right on time, and drove us to act based on those best practices, as I believed (and still believe) that pagination is critical to get right by retailers & ecommerce businesses. By the time BrightonSEO 2020 arrives, it will be over a year since the announcement, and I'd like to review the UK retail ecommerce sector, to see how many did something about it, best practices, bad practices, ugly practices, and give some tips based on what we did at SilkFred including how that worked out for us.
Download the full write up with embedded slides here: https://bit.ly/ecom-pagination
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.
This document provides information on advanced Google searching techniques. It discusses how search engines work and user expectations. Various search operators and strategies are described, such as phrase searches, Boolean operators, title searches, URL searches, and site-limited searches. The document recommends beginning with a title field search using Boolean expressions that is limited to a top-level domain or specific website to find the most relevant information.
Search and social patents for 2012 and beyondBill Slawski
The document summarizes Bill Slawski's presentation on search and social media patents from 2012 and beyond. It discusses various patents Google has acquired related to search, social media, hardware, fiber optic networks, and more. It also outlines patents for phrase-based indexing, concept-based indexing, ranking pages based on user interactions, building a knowledge graph, and developing a planet-scale distributed search index. Slawski suggests Google may expand into hardware, entertainment, internet service provision, and more based on its patent portfolio.
This document provides information about searching online, including:
- The size of the internet has grown tremendously, making proper searching skills more important.
- IPV6 was launched in 2012 to accommodate more internet addresses as devices increase.
- Search engines, directories, and databases are described as important tools for online research. Keywords, boolean searchers, and other search techniques are also outlined.
- Criteria like authority, purpose, currency and bias are important to evaluate sources found in online searches.
The document discusses evaluating online sources and provides examples of search techniques using Google and Bing to find information on topics like Martin Luther King Jr. and conversions between measurements. It also covers evaluating the credibility of websites and using subject specific search engines or limiting searches to particular domains or file types.
Search Analytics: Conversations with Your Customersrichwig
1. The document discusses analyzing search logs to understand how users interact with search engines and how to improve search and site organization based on these insights.
2. Key insights that can be gained from search log analysis include popular search terms, queries that return no results, frequently clicked search results, and patterns in search behavior over time and between user groups.
3. Information from search log analysis can be used to improve search features, results presentation, site navigation, metadata, and content.
The document discusses search engines and how they have evolved over time. It explains that early search engines ranked results based mainly on content, while modern engines also consider factors like page structure, popularity, and reputation. The document provides definitions of key search-related terms and outlines some of the main components and processes involved in how search engines work, such as crawling websites, indexing pages, and ranking results. It also discusses different types of search tools and how to choose the best one depending on your information needs.
The document summarizes research in semantic search and its applications. It discusses the evolution of semantic search from early work on the semantic web to current applications using knowledge graphs. It outlines key challenges in semantic search like query understanding and how mobile search is driving new areas like conversational agents and task completion. The use of semantic representations and knowledge bases is helping to improve search quality and enable new interactive applications.
The document provides an overview of search engines and search algorithms. It discusses (1) the key concepts of search including user intent, queries, documents and results; (2) the technical aspects such as indexing, ranking, and learning algorithms; and (3) current and future challenges for search. Learning algorithms covered include pointwise, pairwise, and listwise approaches. The goal of search engines is to accurately match user intent with relevant documents from a large corpus.
The document discusses semantic search capabilities at Yahoo. It describes how Yahoo has developed techniques to extract structured data and metadata from webpages to power enhanced search results. This includes information extraction, data fusion, and curating knowledge in a graph. Yahoo uses this knowledge to better understand search queries and present relevant entities and attributes in results. Semantic search remains an active area of research.
Making the Web Searchable - Keynote ICWE 2015Peter Mika
This document discusses making the web more searchable through semantic technologies. It begins with an overview of how web search currently works and its limitations, and then discusses how the semantic web aims to address these issues by adding explicit meaning and relationships between data on the web. It describes early skepticism of the semantic web from the information retrieval community and how it has become more practical over time. It also outlines research into semantic search done at Yahoo, including developing a knowledge graph and using semantic information to enhance search results. Finally, it discusses how semantic technologies are now being adopted more widely through efforts like schema.org.
(Keynote) Peter Mika - “Making the Web Searchable”icwe2015
This document discusses making web search more intelligent through semantic search techniques. It begins by describing how current web search works but has limitations due to not understanding context and meaning. The promise of the semantic web to address this through shared identifiers and structured data is then presented. However, challenges have prevented it from being fully realized. The document outlines research at Yahoo on semantic search, including exploiting semantic models and metadata to enhance search results. This involves techniques such as knowledge graphs, which can provide important entity information to better satisfy user search needs.
Search Strategy for Enterprise SharePoint 2013 - Vancouver SharePoint SummitJoel Oleson
The Four Pillars of Search really help you focus your search planning. In this session we dig into the context, content, metadata and UX or user experience that really matter. We also dig into a variety of publicly accessible SharePoint 2013 real world search pages to demonstrate the value.
Here are some options for completing your query:
- Freddie Mercury was the lead singer of Queen
- Brian May was the guitarist for Queen
- Queen was a British rock band formed in 1970
- Freddie Mercury died in 1991 from complications due to AIDS
This document discusses various techniques for question answering and relation extraction in natural language processing. It provides an overview of question answering systems and approaches, including examples like START, Ask Jeeves and Siri. It also discusses using search engines for question answering, relation extraction from questions, and common evaluation metrics for question answering systems like accuracy and mean reciprocal rank.
This document discusses using Google search as a research tool. It provides examples of query design using advanced search operators to explore issues. Some examples include comparing results from searches that include or exclude specific sites, and searching for "rights" in different languages to analyze how rights issues vary by country. The document also provides a research protocol for using Google search consistently, including logging out of accounts and clearing browser history. It suggests saving and organizing search results to allow verification and retrieval later.
How to evaluate the whole web (without being Google)Dixon Jones
Could you build your own, private view of the Internet? One that isn't reliant on Google or Bing? Majestic has done this and now has one of the largest web indexes on the planet. Whilst known and a backlink analysis engine, Majestic infact has its own, unique view of the Internet and is able to derive meaning, influence and context out of its dataset. Here's how they did it. (2018)
Information Discovery and Search Strategies for Evidence-Based ResearchDavid Nzoputa Ofili
This event was on May 2, 2017 at Wesley University, Ondo State, Nigeria. I trained the university's staff (academic and non-academic) on "Information Discovery and Search Strategies for Evidence-Based Research" in an information/digital literacy session.
y Keynote Presentation from today at SMXL Milan 2019 - Loving Italy about entities and augmentation queries and question answer through building knowledge graphs.
Keyword Research requires knowing your audiences and tasks for each of them. It can include taxonomies, Ontologies, context terms and disambiguation and optimizing for a knowledge graph and finding related entities.
Changes in Structured Data at Google (SEO Camp 'us in Paris)Bill Slawski
This document discusses how Google uses structured data and annotations to power its search results. It describes Andrew Hogue's work using annotation frameworks to link unstructured data to a fact repository known as a knowledge graph. The document outlines several patents related to augmenting queries, generating related questions, and identifying structured data and candidate answer passages to provide contextual search results.
Guidelines and best practices for successful seo william slawski smxl milan...Bill Slawski
About How I started using Entities in Optimizing sites, and How Google has been adding Entities to Search Results and working on Updating its Knowledge Graph.
This document discusses changes in search engine optimization (SEO) and how to cut through noise. It summarizes patents related to ranking news articles over time and how they show changes in what signals are used to evaluate news sources. It recommends optimizing content for things and voice search by adding structured data for entities and speakable schema to help digital assistants answer questions about the content. Additional reading on entity-oriented search, voice search, and leaving no valuable data behind is also provided.
1. The document discusses Sergey Brin's early work on extracting structured data from unstructured sources like the world wide web through his DIPRE algorithm.
2. It then shows how projects at Google like Google Maps and WebTables have built upon this idea to generate structured data from various online sources.
3. Current initiatives at Google like schema markup, question answering, and crowdsourcing ontologies continue working to understand online information in a more semantic, structured way to improve search.
Knowledge Panels, Rich Snippets and Semantic MarkupBill Slawski
My 2016 Pubcon Presentation showing how I incorporate Knowledge Panels, Entities, the Knowledge Graph API, Rich Snippets, Featured Snippets and Structured Snippets in SEO site Audits.
The document discusses how search engines are increasingly providing direct answers to queries in order to make searching quicker and easier. It describes Google's efforts to return direct answers from authoritative sources through techniques like processing sources offline to determine common answers to factual questions. The document also outlines various ways search engines can obtain structured information from sources like knowledge bases, query logs, and tabular data on websites to include as direct answers to users' questions.
The document discusses the history and development of Google's search technology. It describes how Google founders Larry Page and Sergey Brin met at Stanford University and collaborated on early search projects. It then outlines key milestones in Google's search capabilities, including the development of PageRank, knowledge graphs, and using contextual information to better understand user queries.
The document summarizes a presentation given by Bill Slawski at the Semantic Technology & Business Conference in San Jose. The presentation discussed how adding semantic information and structuring content around entities can help websites better optimize for search engines and provide more relevant experiences for users. It also provided several examples of how search engines are using entities and knowledge graphs to enhance search results and anticipate related queries.
Content Audits for SEO & Site Migration: Picking a website up on your back an...Bill Slawski
Once I was tasked as part of a team moving a large Public Courthouse to a new location. It's something I'll always remember, and I'm reminded of it every time I'm involved in the migration of a new site to a new domain. Success is in the planning, and in successfully tackling small details.
First question I asked everyone is, "How many of you have never moved to a new home? Moving a courthouse is a whole lot more work." No one raised their hand. They can related to the challege.
Google Will Not Go Gentle into That Good Night: Project GlassBill Slawski
My presentation slides from SMX East on future search interfaces on a conceptual level, and how spoken, visual, and even parameterless searches may impact seo and online marketing.
Everything you wanted to know about crawling, but didn't know where to askBill Slawski
Crawlers and spiders were developed in the early days of the web to index important web pages. Key factors for important pages included containing relevant words, having many backlinks and a high PageRank. Search engines developed ways for crawlers to identify and prioritize important pages through techniques like following links and analyzing site structure. Techniques like XML sitemaps and rel="canonical" help crawlers understand a site's structure and identify the best version of a page. Social media is also now being analyzed to help determine page importance. Crawlers have become more sophisticated over time but still rely on techniques like following links and analyzing site structure and links.
Promoting the blog with search engine optimizationBill Slawski
The document provides tips for using blogging to promote a business through search engine optimization and marketing. It discusses identifying business objectives for the blog, learning about competitors, defining a unique selling proposition, brainstorming topics related to the business or customers, exploring keyword opportunities, and including features that make it easy for readers to share the content. The overall goal is to educate, engage, and entertain customers through relevant and interesting blog content.
Have you ever built a sandcastle at the beach, only to see it crumble when the tide comes in? In the digital world, our information is like that sandcastle, constantly under threat from waves of cyberattacks. A cybersecurity course is like learning to build a fortress for your information!
This course will teach you how to protect yourself from sneaky online characters who might try to steal your passwords, photos, or even mess with your computer. You'll learn about things like:
* **Spotting online traps:** Phishing emails that look real but could steal your info, and websites that might be hiding malware (like tiny digital monsters).
* **Building strong defenses:** Creating powerful passwords and keeping your software up-to-date, like putting a big, strong lock on your digital door.
* **Fighting back (safely):** Learning how to identify and avoid threats, and what to do if something does go wrong.
By the end of this course, you'll be a cybersecurity champion, ready to defend your digital world and keep your information safe and sound!
Megalive99 Situs Betting Online Gacor TerpercayaMegalive99
Megalive99 telah menetapkan standar tinggi untuk platform taruhan online. Berbagai macam permainan, desain ramah pengguna, dan transaksi aman menjadikannya pilihan utama para petaruh.
5. The Metamorphosis is one from
indexing a Web of URLs to indexing a
Web of Named Entities
7. Why Change?
• The Inventor of the Web Updated it
• Microsoft was going there
• Google wanted to answer questions
• Yahoo introduced a Web of Concepts
• People search for Named Entities
15. Hello Direct Answers
“Now we are increasingly able to provide
direct answers -- even if you’re speaking
your question using Voice Search --
which makes it quicker, easier and more
natural to find what you’re looking for.”
Google FORM 10-K for 2014 (Fiscal Year December 31, 2014)
16. Direct Answers
“In each case we’re trying to get you
direct answers to your queries because
it’s quicker and less hassle than the
ten blue links Google used to show.”
We built Google for users, not websites
Eric Schmidt – Google Europe Blog 9/6/2014
18. Search engines like Google, Yahoo! and Bing have
already started displaying richer information for
some search queries, including maps and weather
(for location searches), reviews and prices (for
product search queries), and profiles (for people
searches).
A path already started?
19. What is a Named Entity?
Specific People, Places, and Things
including brands (Thanks, Rand!)
And Entities show up in Queries!
20. According to an internal study of Microsoft, at least
20-30% of queries submitted to Bing search are
simply name entities, and it is reported 71% of
queries contain name entities.
~ Building Taxonomy of Web Search Intents for
Name Entity Queries
21. Google Introduced a Knowledge
Graph filled with Named Entities
(May 16, 2012)
27. Best Answers to Fact Queries
Selecting the best answer to a
fact query from among a set
of potential answers
Invented by: Douglas L. T.
Rohde, Thomas W. Ritchford
Assignee: Google Inc.
US Patent 7,953,720
Filed: March 31, 2005
Granted: May 31, 2011
28. Fact Queries
• A Fact Repository is searched to find
an answer
• Potential Answers are scored based
upon confidence and importance.
• The Best scored answer is displayed.
31. Answer Box Snippets
Determination of a desired
repository
Invented by Michael Angelo,
David Braginsky, Jeremy
Ginsberg, and Simon Tong
US Patent Application
20070005568
Published January 4, 2007
32. Answer Box Snippets
• Google may display an answer box from Different
Repositories: Local, News, Books, Images, Shopping
• They fulfill Universal Search Results
• At 2006 SES, were referred to as “Vertical Creep
into Search Results” and “Answer Box” Results.
34. Intent Queries
Natural Language Search Results for
Intent Queries
International Filing Date: 23.05.2014
Inventors: Tomer Shmiel, Dvir Keysar,
and Yonatan Erez
35. Natural Language Intent Queries
• Natural language Questions
• Answers from Authoritative Resources
• Answers from a Data Store of headers and answers (Like
in an FAQ)
• Answers follow an Intent Format; “what are the
symptoms of X”, “X Treatment”
• Answers fit into a results format like Search Result
Snippets
37. Look at the sources…
http://www.webmd.com/a-to-
z-guides/dehydration-adults
38. Triggering Answer Boxes
Triggering Answer Boxes
Invented by Tal Cohen,
Ziv Bar-Yossef, Igor Tsvetkov,
Adi Mano, Oren Naim, Nitsan Oz,
Nir Andelman, Pravir K. Gupta
US Patent Application
20150169750
Published June 18, 2015
39. Triggering Answer Boxes
• Review Search Results for Any that might Trigger
Answer Box Results; Search Result for “Duke Blue
Devils” could be Football or Basketball, so answer
box would be, too.
• Review Topics Associated with Search Results (news,
sports, weather, television)
• These Answer box results might be personalized.
41. Rich Content for Answers
Rich Content for Query Answers
Pub. No.: WO/2015/102869
Invented by: Gal Chechik, Eyal
Segalis, Yaniv Leviathan, and
Yoav Izur
International Filing Date: 15.12.2014
42. Rich Content for Answers
• Answers may go beyond Text to Images, Video,
Audio
• Rich Results may be based upon contextual queries
that combine the questions and answers
• An image result for “What is the capital of
California?” might use “capital of California,
Sacramento” as the query
43. Entity Triggering
Using an entity database to answer
entity-triggering questions
Inventors: Melissa K. Carroll, and
John J. Lee
US Patent 9,081,814 –
Granted July 14, 2015
44. Entity Triggering
• Similar to Fact Repository Approach
• Google looks for entities in queries.
• Google tries to including those entities in the
answers.
• “Identifying” the entities may help lead to answers
• Answers may be values of attributes of those
entities, such as, “What is the population of LA?”
46. Combining Data & URLs
• Rich Snippets – Uses Data from Schema
Vocabulary
• Structured Snippets – Uses Data from
Tables
47. Data + URLs (Rich Snippets)
http://schema.org/
48. Rich Snippets
Generating specialized search results in response to patterned queries
Invented by Nicholas Brock Weininger, and Ramanathan V. Guha
US Patent 7,593,939 Granted September 22, 2009
53. Structured Snippets
Applying WebTables in Practice, by Sreeram
Balakrishnan, Alon Halevy, Boulos Harb, Hongrae
Lee, Jayant Madhavan, Afshin Rostamizadeh,
Warren Shen, Kenneth Wilder, Fei Wu, Cong Yu, 7th
Biennial Conference on Innovative Data Systems
Research (CIDR ’15) January 4-7, 2015,
Asilomar, California, USA
54. Structured Snippets
• Use Tables on your pages filled with data
• Use markup like Table Headings<th> for rows of the
tables
• Use Titles and captions descriptive of the Table’s
data
• Use Associated text on the Page the table is about
to help Google better understand it.
55. Further Reading on Google Answers
• Deep Learning - Teaching Machines to Read and
Comprehend (Deepmind)
• Information Extraction - Open Information
Extraction from the Web (Wavii)
• Tables - Applying WebTables in Practice (Alon
Halevy)
• Entities - Use These Tools To See What Entities Are
On A Web Page (Barbara Starr)
56. Questions
• Bill Slawski, Director of Search Marketing, Go Fish
Digital
• Editor of SEO by the Sea
• https://plus.google.com/u/0/+BillSlawski/posts
• https://twitter.com/bill_slawski
• https://www.facebook.com/bill.slawski