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.
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.
Coronavirus and Future of SEO: Digital Marketing and Remote CultureKoray Tugberk GUBUR
I have attended a great SEO and Digital Marketing webinar with Founder of Stradiji and SEMRush Turkey Lead Mr. Mert Erkal and My Dearest Friend and SEO Consultant Atakan Erdoğan.
Small Note: After I uploaded the presentation, Google launched a new Covid-19 news address like Bing/covid-19. You may want to look at it -> https://www.google.com/covid-19
I have prepared a Presentation about Coronavirus's Effects on Search Engine Optimization (SEO).
You will find Coronavirus's changing effects on Digital Marketing and psychology of global society while using Search Engines.
I also have focused on Search Engine's and Social Media Brands, E-commerce Site's reflexes against Coronavirus Pandemic.
You will see the web sites and categories who earn more traffic and lose traffic. You will also see conversion rate differences because of Coronavirus.
Also, I have told about Search Engine's differences and their attitude against the Coronavirus Pandemic, their future, their updates during the pandemic.
In the last part, you will see some new 2020 Web Technology and Design Trends with AI.
There are also Google Researches for better Search Engine technologies.
Questions:
1- What are the differences between Yandex, Google, Bing, and Duckduckgo for Coronavirus Pandemic?
2- Twitter, Instagram, Amazon or Apple, what are they doing?
3- What do people search most for during the Coronavirus Crisis?
4- What changes from country to country?
5- What are the future technologies of Web and App?
6- How and why do Search Engines improve AI, what is the last events?
7- Which sites loose traffic and which earn more?
8- Lots of quotes from International SEOs about the pandemic.
And more...
I am Koray Tuğberk GÜBÜR and a Holistic SEO Expert.
I sincerely thank you for my Dearest Friend Atakan Erdoğan and Mr. Mert Erkal for this awesome webinar opportunity and experience.
To watch the webinar, please visit Stradiji's Official Youtube Channel.
https://www.youtube.com/watch?v=V4sJTNcRqaM&t=100s
How to Automatically Subcategorise Your Website Automatically With Pythonsearchsolved
The document describes a Python script that can automatically generate new subcategories for an ecommerce website based on clustering product names. It discusses:
- Using NLTK to generate n-grams from product names to cluster related products
- Filtering the n-grams to keep only those with commercial value by checking for search volume and CPC data
- Running the script on a large home improvement site to identify over 1,650 new subcategory opportunities with a total search volume of over 13 million
- Sharing the script so others can automate subcategory identification for their own sites to scale up an important SEO tactic.
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.
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.
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.
Internal Linking - The Topic Clustering Way edited.pptxDixon Jones
This document discusses internal linking strategies and techniques. It begins by explaining the benefits of connecting entities within content, rather than just words, and translating those connections into internal links. It then provides an overview of technologies like PageRank, the reasonable surfer algorithm, topical PageRank, chunking, and natural language processing that search engines use to understand contexts and how those ideas can be applied to internal linking at scale. Specific options for approaches to internal linking existing pages are also outlined.
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.
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsDan Taylor
My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.
Probabilistic Thinking in SEO - BrightonSEO October 2022Andrew Charlton
Andrew talks through practical ways to think in probabilities to make better decisions in SEO at BrightonSEO.
From methodologies to help you increase the chances of successful outcomes, to reframing decisions as bets to prioritise your recommendations.
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.
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.
BrightonSEO October 2022 - Log File Analysis - Steven van Vessum.pdfSteven van Vessum
This document discusses how log file insights can help companies improve their crawling, indexing and organic marketing performance. It outlines some of the common issues companies face like not understanding search engine behavior and not reflecting on their past work. With log file insights accessible in real-time and automatically distilled, companies can answer critical questions to speed up their crawl times, see how search engines are handling their updated content and troubleshoot issues. The presenter promotes their solution, ContentKing, which provides real-time log file analysis from CDN logs to help companies learn what search engines know and keep sharpening their SEO strategies.
Accessibility, strategy and schema - do they go hand in hand? Beth Barnham Br...BethBarnham1
In this talk, I explore schema and its link to online accessibility. Can schema really help the web to be more accessible? And how should we strategise this as SEOs? Strategy plays a vital role in SEO but often times the technical areas are overlooked within a wider marketing strategy
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.
This document discusses the value and uses of hyperlinks. It explains that hyperlinks can help understand a person's influence, interests, and peers. Hyperlinks also allow you to find influencers around a topic or identify the authors of a news site. The key takeaway is that having access to true link data and understanding how to analyze link information can provide new insights.
This document discusses digital marketing strategies focused on establishing authority through valuable, timeless content. It recommends creating content such as articles, videos, and academic papers on topics that will remain relevant for years to establish expertise. Creating a steady stream of high-quality content over time builds an online presence and credibility without major risks of losses, and may lead to job offers, clients, or other opportunities. It provides examples of interactive dashboards and open-source software that gained popularity and users by continuously publishing improvements and documentation without needing to rely on things like resumes or company profiles.
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
PubCon, Lazarina Stoy. - Machine Learning in Search: Google's ML APIs vs Open...LazarinaStoyanova
The document discusses Google's ML APIs versus OpenAI's APIs and their applications for SEO and digital marketing tasks. It provides examples of how natural language processing APIs from Google and OpenAI can be used for tasks like text analysis, sentiment analysis, document classification, translation and content transformation. While both Google and OpenAI APIs are useful, the document recommends choosing the right API for each specific task based on its capabilities and limitations in order to get the best results.
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.
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)
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.
‘How Topics Affect Everyone and Everything’ by Dixon Jones - Marketing Direct...Jess Melia
Majestic SEO surveys and maps the Internet, creating the largest commercial Link Intelligence database in the world. It’s Marketing Director, Dixon Jones, explores with our travel sector guests why our brains need trusted groups to help make good decisions, why Computers need trusted groups to be effective search engines and how search engines can analyze people and pages to categorize anything and everything on the web.
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.
Data Informed Design - Good Tech Test - May 2018Courtney Clark
When it comes to design, everyone has an opinion! However, during reviews and discussions it’s those with more than an opinion that fair the best. Successful design solutions require a deep understanding of audiences, clear strategy, and good ole data.
In this session you’ll learn:
- Common data sources for design
- How to build a data-informed approach (not data-driven)
- What data-informed design looks like in the wild (aka case studies).
Whether you’re trying to prove a point, make an improvement, or discover something new, data-informed design moves your team from gut-feelings to fact-based decisions.
The document provides guidance on evaluating the credibility of internet sources. It discusses how information can spread quickly online before being verified. Readers are encouraged to scrutinize sources by considering whether the author is an expert, has an agenda, and cites evidence. Search engines like SweetSearch that curate reliable sources are recommended. The conclusion emphasizes that credible ideas will be supported by many trustworthy sources and the scientific process rather than just emotion or a single perspective.
The document discusses determining the credibility of sources when conducting research online. It provides tips for evaluating sources such as checking if the author is a recognized expert, if multiple sources confirm the information, and if the source has biases. The document also recommends specialized search engines like SweetSearch and FindingDulcinea that curate reliable newspaper articles. Certain domains like .edu, .gov and websites from reputable organizations can also be more trustworthy sources of information. Students are advised to exercise critical thinking and verify facts from multiple credible sources.
This document discusses search engine optimization and the development of search systems. It notes that computer science has directed search system development with a focus on results relevance, while neglecting user experience. The intent is to inspire deeper engagement in designing search experiences that do more than just sell products. It also discusses challenges like the volume of online information, differences in language and perception, and the limitations of current search systems.
Advanced Keyword Research SMX Toronto March 2013BrightEdge
The document discusses several concepts for ranking search results, including:
- Documents containing more query terms are considered more relevant. Longer documents are discounted.
- Hilltop introduced the concept of "authority" to determine relevance based on the number of unaffiliated pages linking to a page on the same subject.
- Google artificially inflates Wikipedia results because it views Wikipedia as an authoritative resource. However, Wikipedia is not infallible.
This presentation looks at new methodologies of keyword research to meet the linguistic and semantic sophistication that is Web search today. Search engines are changing and SEO must change with them to meet the challenge of getting the right visitors to the site.
Kristian Norling will present on trends in enterprise search based on a 2012 global survey. The presentation will discuss how growing amounts of content are making it difficult for users to find the right information. It will also explore how context, search analytics, trustworthy information, social search, and mobile can help improve enterprise search and findability. The full report from the 2012 Enterprise Search and Findability Survey will be available to attendees.
This document summarizes a discussion between Christy Gilchrist from Wellspan Health and Todd Tullis from goBalto on using site intelligence and predictive analytics to improve clinical trial feasibility assessments, site selection, startup, and performance evaluation. Some key points discussed include:
1) Using data analysis of electronic health records and epidemiological models to better predict patient enrollment expectations and feasibility at sites.
2) Measuring site and sponsor responsiveness to startup tasks in real-time to facilitate faster resolution of issues.
3) Evaluating site performance against enrollment goals, compliance goals, and business goals to help sites improve for future trials.
4) Sharing post-study performance data with sites to build
This document provides information for a lesson plan titled "Become an Online Sleuth". The lesson teaches students how to evaluate the credibility of online content by identifying guidelines for what makes a website trustworthy or not. It involves students watching a video about evaluation criteria, discussing factors that indicate a site is reputable, and having students practice analyzing websites. The lesson aims to help students understand that just because something is online does not automatically make it true, and to learn skills for determining which sources they can trust online.
What IA, UX and SEO Can Learn from Each OtherIan Lurie
Google has become the arbiter how users experience a website. Their data-driven determinants of what constitute good UX directly influence how a site is found. This is wrong because people, not machines, should determine experience; Google does not tell the SEO or UX community what data is used to measure experience and many elements of experience cannot be measured.This presentation reveals why Google uses UX signals to determine placement in search results and how to create a customer pleasing and highly visible user experience for your website.
Bearish SEO: Defining the User Experience for Google’s Panda Search LandscapeMarianne Sweeny
The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.
Similar to Opinion-based Article Ranking for Information Retrieval Systems: Factoids and Facts (20)
10th International Conference on Networks, Mobile Communications and Telema...ijp2p
10th International Conference on Networks, Mobile Communications and
Telematics (NMOCT 2024)
Scope
10th International Conference on Networks, Mobile Communications and Telematics (NMOCT 2024) is a forum for presenting new advances and research results in the fields of Network, Mobile communications, and Telematics. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works, and industrial experiences that describe significant advances in the following areas but are not limited to.
Topics of interest include, but are not limited to, the following:
Mobile Communications and Telematics Mobile Network Management and Service Infrastructure Mobile Computing Integrated Mobile Marketing Communications Efficacy of Mobile Communications Mobile Communication Applications Critical Success Factors for Mobile Communication Diffusion Metric Mobile Business Enterprise Mobile Communication Security Issues and Requirements Mobile and Handheld Devices in the Education Telematics Tele-Learning Privacy and Security in Mobile Computing and Wireless Systems Cross-Cultural Mobile Communication Issues Integration and Interworking of Wired and Wireless Networks Location Management for Mobile Communications Distributed Systems Aspects of Mobile Computing Next Generation Internet Next Generation Web Architectures Network Operations and Management Adhoc and Sensor Networks Internet and Web Applications Ubiquitous Networks Wireless Multimedia Systems Wireless Communications
Heterogeneous Wireless Networks Operating System and Middleware Support for Mobile Computing Interaction and Integration in Mobile Communications Business Models for Mobile Communications E-Commerce & E-Governance
Nomadic and Portable Communication Wireless Information Assurance Mobile Multimedia Architecture and Network Management Mobile Multimedia Network Traffic Engineering & Optimization Mobile Multimedia Infrastructure Developments Mobile Multimedia Markets & Business Models Personalization, Privacy and Security in Mobile Multimedia Mobile Computing Software Architectures Network & Communications Network Protocols & Wireless Networks Network Architectures High Speed Networks Routing, Switching and Addressing Techniques Measurement and Performance Analysis Peer To Peer and Overlay Networks QOS and Resource Management Network-Based Applications Network Security Self-organizing networks and Networked Systems Mobile & Broadband Wireless Internet Recent Trends & Developments in Computer Networks
Paper Submission
Authors are invited to submit papers through the conference Submission System by July 06, 2024. Submissions must be original and
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seo proposal | Kiyado Innovations LLP pdfdiyakiyado
Crafting a compelling SEO proposal? Learn how to structure a winning SEO proposal template with essential elements and tips for client engagement. Elevate your SEO strategy with expert insights and examples
The advent of social media has revolutionized communication, transforming the way people connect, share, and interact globally. At the forefront of this digital revolution are visionary entrepreneurs who recognized the potential of the internet to foster social connections and create communities. This essay explores the founders of some of the most influential social media platforms, their journeys, and the lasting impact they have made on society.
Mark Zuckerberg, along with his college roommates Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes, founded Facebook in 2004. Initially created as a social networking site for Harvard University students, Facebook rapidly expanded to other universities and eventually to the general public. Zuckerberg's vision was to create an online directory that connected people through their real-life social networks.
Twitter, founded in 2006 by Jack Dorsey, Biz Stone, and Evan Williams, brought a new dimension to social media with its microblogging platform. Dorsey envisioned a service that allowed users to share short, real-time updates, limited to 140 characters (now 280). This concise format encouraged rapid sharing of information and fostered a culture of brevity and immediacy.
Kevin Systrom and Mike Krieger co-founded Instagram in 2010, focusing on photo and video sharing. Systrom, who studied photography, wanted to create an app that made mobile photos look professional. The app's unique filters and easy-to-use interface quickly gained popularity, amassing over a million users within two months of its launch.
Instagram's emphasis on visual content has had a significant cultural impact. It has popularized the concept of influencers, giving rise to a new industry where individuals can monetize their popularity and reach. The platform has also revolutionized digital marketing, enabling brands to connect with consumers in more authentic and engaging ways. Acquired by Facebook in 2012, Instagram continues to be a dominant force in social media, shaping trends and cultural norms.
Reid Hoffman founded LinkedIn in 2002 with the goal of creating a professional networking platform. Unlike other social media sites focused on personal connections, LinkedIn was designed to connect professionals, facilitate job searches, and foster business relationships. The platform allows users to create professional profiles, network with colleagues, and share industry insights.
LinkedIn has become an indispensable tool for job seekers, recruiters, and businesses. It has transformed the job market by making it easier to find and connect with potential employers and employees. LinkedIn's influence extends beyond job searches; it has become a hub for professional development, thought leadership, and industry news. Hoffman's vision has significantly impacted how professionals manage their careers and build their networks.
Jan Koum and Brian Acton co-founded WhatsApp in 2009, aiming to create a simple, reliable..
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and Facts
1. How Search Engines Leverage
Opinion-based Articles for Ranking
Rethinking Search: Corroboration of Web Answers
Koray Tuğberk GÜBÜR
2. Components for Re-ranking based on Opiniated Factoids
01
Uncertain
Inference
Knowledge
Base
02
Corroboration
of Web
Answers
03
Embarrassment
Factor
04
Open Information
Extraction
5
External
Databases
6
7
Evidence
Aggregation
09
9
Information
Literacy
06
07
08
10 Semantic Role
Labeling
Truth Ranges
05
3. Uncertain Inference
• Uncertain Inference is found by C. J. Van
Rijsbergen from Glasgow University.
• Focuses on “Query Inference” with
“Context Understanding”.
• Query Path, and Query Context (Context-
Sensitive Search Elements) are used.
• Query is processed with Probable
Probabilities for Question Generation.
• It requires a “Knowledge Base” for
understanding Factual Needs for the query.
• “Uncertain facts” have a plausibility
threshold that gives “Opinions” to exist on
results.
• Extract word sequences in News Titles.
How do Search Engines know facts?
Andrew Houge
The Structured Search Engine
4. Uncertain Inference
How do Search Engines know facts?
Andrew Houge – The Structured Search Engine
• Query Processing and Parsing is
another topic.
• But, to reach out to “wrong” and
“true” facts, the high level of
confidence and coverage are
needed.
• The Uncertain Inference follows
users’ behaviors in “Adaptive
Search”, or sometimes, it uses
“word-sequences” in a mega corpus.
• Extract, Entity-Attribute Pairs and
their synonyms from News Articles.
5. Knowledge Base
• Different than Knowledge Graph.
• Stores facts, or factual values for the
same entity-attribute pairs, and
triples.
• It is dynamic.
• A fact from today might be
inaccurate information tomorrow.
• Procedural Part of Knowledge Bases
helps to update the connections
between components.
• Understand which facts are
approved by search engine.
Browsable Fact Repository
6. Corroboration of Web Answers
• One of the best 10 “Opinion Papers” in
Information Retrieval.
• Directly connected to the concept of
“Helpful Content”, or “Information
Responsiveness”.
• “Even, main web source has
contradicting information for the same
question, which one is fact?”.
• Corroboration of Web Answers focus on
“Truth Ranges”, and “Answer
Prominence” to choose answers from
certain sources.
• Create your own truth range by
auditing ranking resources.
How do Search Engines know facts?
7. Corroboration of Web Answers
• Minji Wu, and Amelia Marian focus on
numeric values and measure units to
find real authorities.
• PageRank, Source Authority, First
Answer, Closeness to First Answer and
De-duplication are used to determine a
“Fact Range”, or “Truth Range”.
• The “Truth Range” changes from today
to tomorrow according to ranking
sources
• Use numeric values, metrics, dates, and
measurement units to have higher
precision.
How do Search Engines know facts?
8. Corroboration of Web Answers
• Google cited the research paper
of “Corroborating Answers from
Multiple Web Sources” more than
40 times in “Candidate Answer
Passage” patent series.
• It is used in Featured Snippets
(Web Answers) since 2018.
• This brings us to “Embarrassment
Factor”.
• Use “safe” and “indirect” answers
for conflicted issues.
How do Search Engines know facts?
9. Embarrassment Factor
• What is Embarrassment Factor?
• Does a Search Engine get shame?
• Can you make a search engine feel shame
with your bad answer, or opinion?
• What happens if you tell that “Barrack
Obama is a communist” in a featured
snippet? Or, “Global Warming is hoax”, or
“Vaccines are for controlling your brain”.
• Let’s remember, “Truth Ranges”.
• Do not play with the patience of search
engine engineers. Do not take advantage
of fundamental NLP understanding.
How do Search Engines know facts?
10. Truth Ranges
• Fuzzy Logic is used.
• Not every wrong is equal.
• Some facts are more facts.
• Some opinions are accepted as consensus.
• Upper and Bottom Limits are used to
determine “safe opinions”.
• Google created “Content Advisories” to
help for “Information Consensus”.
• Stay in the consensus (reports with
descriptive news), unless it is “satiric”
(critiques with questions).
• Use “question-format” as a shield against
algorithms, if you are outside of truth
ranges.
Which one is more factual?
Source: Wesley Chai
11. Truth Ranges
• There are two different approaches
in Linguistics for a “truth”, or “fact”.
• Words like “will”, “can”, “might”, “may”,
“may” decrease the certainty.
• Numeric Ranges, or Sentiment
Magnitude and Direction are used.
• The middle of range is called
“Fixpoint”.
• The answers that are outside of
Range is filtered out.
• Find the balance between
“precision” and “coverage” in news
titles, and intros.
How do Search Engines know facts?
12. Truth Ranges
• According to Fuzzy Logic:
• 1 > 5 and 1 > 10 are not equally wrong.
• One of them is more wrong than other.
• For “Disagreeing Views”,
“Corroboration” happens with
inference.
• “Barrack Obama is born in Hawaii”,
• “Barrack Obama is born in Kenya”.
• A search engine might see “Barrack
Obama is a US Citizen” as a safe answer to
give to avoid embarrassment.
• Use the absolute truths, for projecting
a safe answer rather than giving a
possible wrong factoid.
Journalists share organization’s trustworthiness
Source: Indiatimes
Source: Making Better Informed Trust Decisions with Generalized Fact-Finding
13. Truth Ranges
• Uncertainty is used as a measurement to filter
factoids.
• Phrases like “I am sure”, or “%45 possibility” create
uncertainty.
• Intrinsic Ambiguities decrease the trust to the
source.
• “Who claims what” is key point for fact-finding
algorithms.
• Source Reliability and, “Variance” and “Mean”
values are used for “fixpoints”.
• Do not use “I am sure”, or “Pretty sure”, “I think…”,
“In my opinion…”, “It might”, “It may”. Tell whether
the “bomb exploded”, or not. Tell “how many
people died”, do not tell “With %45 possibility,
over 20 people…”
• Compare your numbers, names, dates and places
for an event to your competitors.
“Safe Answers” is better.
Source: Making Better Informed Trust Decisions with Generalized Fact-Finding
CIUV: Collaborating Information Against Unreliable Views
14. Truth Ranges: Why do we need PageRank?
• Speed.
• Google and other search engines do not have time
to process text of the documents.
• News SEO has to prioritize “indexing”.
• News Search Engine has to serve everything in
fastest way.
• Processing the text, checking accuracy is not
possible in seconds, minutes, or hours and days,
when a source publishes 100,000 words a day.
• Thus, Truth Ranges is a “long-term ranking factor”
for news sources.
• Google gets angry when I give PageRank related
suggestions.
• Understand that, some sources are prioritized,
even if they scrape and use your original news
story.
Groundedness - Unanimity
Source: Towards an axiomatic approach to truth discovery
Source: Towards an axiomatic approach to truth discovery
15. Truth Ranges: Why do we need PageRank?
We guess that this news is quality…
Source: Corroborating Information from Disagreeing Views
Source: Corroborating Information from
Disagreeing Views
16. Information Extraction (OIE)
An example of OIE
• Open Information Extraction is found
by WAVII.
• WAVII is bought by Google for $30
Million.
• It is used to expand Google’s
Knowledge Graph.
• OIE is to extract triples, and recognize
minor entities to structure a semantic
network.
• Extract “predicates” from news
articles. Create tuples from
“predicates, nouns, and subjects”.
• Understand which fact, or factoid is
given first, or later.
Open Information Extraction Example from the researchers.
17. Information Extraction (OIE): Rel-grams
Precision / Coverage
• Open Information Extraction is to
extract opinions, and facts about
certain concepts, and named entities.
• It uses “tuples” as “predicate” and
“noun”.
• Aggregates occurrences, standardizing
the masked sections by comparing the
different OIE iterations.
• Match “prepositions” to
“interrogative” terms.
• Use “uncertain inference” to extract
interrogative terms.
18. Information Extraction (OIE): Rel-grams
Word Connections and Sense Disambiguation
• OIE is used by Google to recognize and
understand micro entities, and knowledge on
the web.
• OIE is helpful for processing the text in the
news sources to understand latest changes in
real-world, and reflect it on the knowledge
base.
• Open Information Extraction is different than
Information Retrieval.
• The opinions and facts of web sources are
compared to each other to understand the
higher groundedness.
• Update outdated facts in your website. “X
lives in P” declaration might be wrong, if “X”
is not alive anymore. How many “died in”
entity lives in your internal knowledge base?
19. External Databases (Data Commons)
Structuring the Web
• Data Commons is aggregation of
unified databases for nearly every
topic, industry, geography and
entity.
• It is a common fact repository that
is open to all web.
• It is supported by Ramanathan V.
Guha.
• It focuses on statistical data.
• Query external databases for
“statistics” to create statistic-rich
news articles.
20. External Databases (Data Commons)
How do Search Engines know facts?
• Google integrated Data
Commons Project to its own
algorithms.
• The announcement is done by
Prabhakar Raghavan.
• It helps to understand accuracy,
and authority of an information
source.
• A trustworthy news article
propagate its trust to next news
article.
22. External Databases (Data Commons)
“As we may think”
“Google is planned to be third-part of your brain”
- Sergey Bring
“Google is designed as a Star Trek Computer to
answer your needs.
It is not created for websites, it is created for users.
- Larry Page
“They already hate Google, so what is the down-
side?”
- Craig Nevill-Manning
23. Semantic Role Labeling
Which news source reflected emotions?
• Words’ order change, but sentence’s
meaning stay same.
• Same opinion can be expressed in
many different ways.
• XYZ corporation bought the stock.
• They sold the stock to XYZ corporation.
• The stock was bought by XYZ corporation.
• The purchase of the stock by XYZ
• corporation ...
• The stock purchase by XYZ corporation ...
• OIE provides an aggregation for
tuples, and relational n-grams to
extract factual propositions.
• Semantic Role Labels help for
standardization based on
“predicates”.
• Match “emotions” to “causes” with
shorter declarations, stay away from
“nested declarations”.
Semantic Role Labeling as Dependency Parsing: Exploring
Latent Tree Structures Inside Arguments
24. Semantic Role Labeling
Agent – Predicate - Theme
• Predicates can take multiple
arguments.
• Semantic role labels are descriptions of
the semantic relation between the
predicate and its arguments.
• Semantic Roles are abstract
representations of the role that an
argument plays in the event described
by the predicate.
• Semantic Role Labeling assigns roles to
the constituents of a sentence.
• Semantic selection restrictions allow
words to have semantic contractions on
the semantic properties.
• Understand “patterns of human
mind”. Reflect these patterns in news
articles, according to “macro-
context”.
25. Semantic Role Labeling
Predicate is context.
• Let’s say, “George Bush” phrase appeared
500,000 times in the News Titles.
• Google has to categorize them according to the
news contexts.
• “Context-based Person Search” is used for this
task.
• But, News Search Engines have to be fast.
• There is no time for processing the text.
• But, “SRL” is a quick process.
• Check Semantic Role Label of Entity, is it agent? Or, is it
theme?
• Which instrument is used?
• Which goal is mentioned?
• Which propositional structure is used?
• For the sentence “George Bush signed military
operation”, the “Relational Grams”, “Aggregated
Tuples”, and “Semantic Role Labels” help a
search engine to differentiate entities/context from
each other.
• “Grouping entities” is not enough. Group
“contexts”. “X and Love Life”, “X and Career”
have different contexts. Connections should
follow “identity” and context together. Analyze
“News Context”, more than “Entity” that
appears.
26. Semantic Role Labeling
How do opinions differ in phrases?
• Beyond Classification:
• It helps to see the factual information.
• It is used to differentiate opinions from
each other.
• It measures the possibility of truth.
• It understands the representation of the
web source according to its connection to
others.
• Semantic Role Labeling is used by
semantic search engines to have
better entity associations.
• The suggested associations, or
graphs are accepted or rejected by
semantic network constructors.
• “Names in the News Title”
should match the Faces in the
News Image.
Source: Marina Santini, Brighton University
Source: Grounded Semantic Role Labeling
27. Question-Answer Pairs
Which evidence is correct?
• Question Generation and Answer
Pairing are NLP tasks for fact
extraction.
• Question generation involves query
parsing and processing.
• Answer pairing involves dense-context
retrieval and question-answer format
matching.
• But, it is not clear which answer is
more accurate.
• Thus, Question-Answer Coverage,
Entity-oriented search and Semantic-
Syntatic Parsing are used.
• Matching entities, attributes,
queries, or phrases are not good
enough, as long as information is
not responsive.
Source: Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
28. Information Literacy - Consensus
Who said it?
• Google started to give education for
Information Literacy.
• It involves recognizing information source
before the information on the source.
• Google ranks News Sources for certain
topics, contexts and entities before ranking
the news.
• The need of “fast indexing and serving” will
always be more important than
understanding the “truth” at the first stage.
• Thus, the quality news sources have higher
accuracy with more historical data, and
PageRank.
• Google has to assume that truth comes
from strength of repeated evidence from
the most authoritative sources.
• Audit “About the source” panels of your
competitors, create a review, and third-
party mention gap.
29. Information Literacy - Consensus
Author Authority?
https://searchengineland.com/what-social-signals-do-google-
bing-really-count-55389
• Danny Sullivan once asked Google
and Bing whether they use social
signals, or author names to
understand who is the real expert on
a topic.
• Both of the search engines said that
they audit “author quality” and
“author expertise” for different
topics.
• Associate authoritative authors
with your web source stronger, if
they are writing for multiple web
sources.
30. Information Literacy - Consensus
How do they use Knowledge Base?
Integrating Knowledge Graph and Natural Text for Language
Model Pre-training
• There are hundreds of different
algorithms to understand the
authenticity and “true facts”.
• For a search engine engineer,
there is no “lie” and “fact”.
• It is only “true facts” and “wrong
facts”.
• And, KELM-like algorithms help
together to differentiate them
from each other.
• Query “Google Knowledge Graph
API” to understand what they
state for the same entity.
31. Information Literacy - Cues
What makes you trustworthy?
• The research that Google cites
mentioned that there are “6 Cues for
snap judgments about whom to trust”.
• These involve “images”, “brands”,
“headlines – tonality”, “social cues”,
“sponsors”, and “interactivity”.
• Google works with MediaWise to
perform surveys and integrate findings
to their own algorithms.
• Create your own “audit templates” for
news articles for these 6 different
verticals. Mark up “MediaWise”.
32. Information Literacy – About this source
Why does your opinion matter?
• The story of “Web Answers” is
too long.
• Context-terms, Topical Entries,
Candidate Answer Passages,
Context-scoring for Candidate
Answer Passages, and many
more concepts…
• Google Product Manager calls
these “word callouts”.
• Search Engine Engineers call
them “representative answer”.
• Learn NLP. Scoring Candidate Answer Passages
33. Some Google Designs
Machine learning to identify opinions in documents
•Identifying opinionated portions in documents
•Relating opinionated portions inside the document
and/or across other documents (e.g., that relate to
the same story)
•To surface opinionated snippets or quotes to users
of a news aggregation.
•To identify portions of a document that convey
opinion.
•Google might rank a source for “report”, but not
for “opinion”. Understand which vertical has a
higher chance for your web source.
34. Some Google Designs
System and method for supporting editorial opinion in the
ranking of search results
“Editorial opinion” without “distorting facts” helps you for ranking.
Especially for “first-person” experience stories, or reviews.
35. Some Google Designs
Embedded communication of link information
“Information in the improved link tags may allow one or more publishers of content and/or
documents to convey opinions about content and/or documents at one or more content
locations and/or one or more document locations. The link tags may also allow one or more
publishers to convey a weighting of the relative importance of one or more content locations
and/or one or more document locations. In some embodiment, at least a portion of the
information in the improved link tags may be encrypted, to allow one or more publishers to
restrict the audience that may view the information in the link tags….. The improved link tags may
allow the publishers to communicate additional information, such as opinions, about the content locations
and/or document locations.”
Categorize boilerplate/main content links according to their context.
“Joe Biden and Congress” might have a different “block-link” than “Joe Biden and Elections”.
36. Some Google Designs
Aspect-Based Sentiment Summarization
Use “key-points” with “sentiments” to summarize essence of news stories.
37. Topicality and Context Filters
Long and Shor Term Solutions for SERP Construction in News Vertical
Short-term Solutions for News Search Engines:
• Classify authoritative sources (PageRank,
Article Count, Unique Sentence Count,
Publication Frequency, Length, Citations,
Search Behaviors).
• Rank authoritative sources for different
topics.
• Classify and rank news web pages according
to their context, and topicality.
• Serve the most relevant news articles based
on trust and confidence.
Long-term Solutions for News Search Engines:
• Process text.
• Understand facts.
• Audit accuracy and comprehensiveness.
• Filter the sources, by re-assigning topical
relevance and authority.