Podcasts about google knowledge graph

In this episode of ACM ByteCast, Bruke Kifle hosts ACM and IEEE Fellow Xin Luna Dong, Principal Scientist at Meta Reality Labs. She has significantly contributed to the development of knowledge graphs, a tool essential for organizing data into understandable relationships. Prior to joining Meta, Luna spent nearly a decade working on knowledge graphs at Amazon and Google. Before that, she spent another decade working on data integration and cleaning at AT&T Labs. She has been a leader in ML applications, working on intelligent personal assistants, search, recommendation, and personalization systems, including products such as Ray-Ban Meta. Her honors and recognitions include the VLDB Women in Database Research Award and the VLDB Early Career Research Contribution Award. Luna shares how early experiences growing up in China sparked her interest in computing, and how her PhD experience in data integration lay the groundwork for future work with knowledge graphs. Luna and Bruke dive into the relevance and structure of knowledge graphs, and her work on Google Knowledge Graph and Amazon Product Knowledge Graph. She talks about the progression of data integration methodologies over the past two decades, how the rise of ML and AI has given rise to a new one, and how knowledge graphs can enhance LLMs. She also mentions promising emerging technologies for answer generation and recommender systems such as Retrieval-Augmented Generation (RAG), and her work on the Comprehensive RAG Benchmark (CRAC) and the KDD Cup competition. Luna also shares her passion for making information access effortless, especially for non-technical users such as small business owners, and suggests some solutions.

Send us a textLink to the Free Knowledge Graph Search Tool - https://audits.com/tools/knowledge-graph-searchSEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.comYou can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO then book an appointment with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Twitter @channel5Find KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

I'm talking with veteran SEO expert Erin Sparks, host of the "Edge of the Web" podcast. Erin shares insider knowledge on building trust with Google and mastering SEO foundations.Key Topics:The Evolution of SEO: Discover how SEO has transformed since 2004, emphasizing accountability and digital strategy. Erin explains the shift from traditional marketing to a data-driven approach, highlighting the importance of staying current in the fast-paced digital landscape.Google's Knowledge Graph: Learn how to leverage Google's Knowledge Graph to enhance your online visibility. Erin discusses how businesses can curate detailed information about their services and products to establish authority and trust with Google.Effective Content Strategy: Erin delves into the importance of content clustering and internal linking. He provides practical tips on organizing your content to improve user experience and increase your website's relevance in Google's eyes.Maximizing Multimedia in SEO: Understand the role of podcasts, videos, and structured data in boosting your SEO. Erin highlights the benefits of hosting content on your website and using structured data to make your content more discoverable.Practical SEO Tools and Techniques: Erin introduces tools like AlsoAsked.com for discovering user queries and content gaps. He also explains the use of structured data and schema to communicate effectively with search engines, enhancing your site's visibility.What you can do:Targeting Non-Branded Keywords: Learn why focusing on non-branded keywords can attract a broader audience and enhance your site's discoverability.Optimizing Internal Links: Find out how to use internal linking to guide users and improve your site's navigability, helping Google understand your content structure.Implementing Structured Data: Erin breaks down the process of using structured data to provide search engines with a clear understanding of your site's content, leading to better rankings.Featured Highlights:ErinSend me a text!The Growth GearExplore business growth and success strategies with Tim Jordan on 'The Growth Gear.Listen on: Apple Podcasts Spotify This Is PropagandaChallenging marketers' delusions about the cultural impact of our work. A WEBBY winner!Listen on: Apple Podcasts SpotifySupport the Show.Search the Simple and Smart SEO Show podcast for something you heard! It's free!Apply to be my podcast guest!

The EDGE of the WEB team ventured across the country to attend the inaugural BrightonSEO U.S conference in beautiful San Diego! This special podcast was filmed LIVE in front of an audience with 5 of the industry's best as panelists. Witness industry experts collaborate in forecasting the unpredictable future of SEO. The panel evaluates the industry's most disruptive topics, including Content at Scale, E-E-A-T, AI Generated Content, SGE, Google's Knowledge Graph, and beyond, offering insights that light the path ahead. Do not miss this very special feature of The EDGE of the Web as we discover the true trajectory of our industry, and SEO's unite to scale the expansive future ahead!   *Thanks to our panelists!* Mordy Oberstein Cindy Krum Julie McCoy Ola King JR Oakes Key Segments: [00:07:20] Panel Segments [00:06:24] Title Sponsor: SE Ranking [00:07:44] How Can We Maintain Creative Control When Using AI To Operate At Scale?  [00:14:52] The Journey Ahead For SEO's As In Relation To Content [00:27:56] How Ca n We Ensure The Accuracy And Reliability Of AI Generated Content?  [00:35:35] The Expanding Google Knowledge Graph [00:45:34] How Is Search Generative AI Going To Transform The Way We Search For Information? [00:55:34] How Will SGE Change Organic Links On The SERP? [01:00:00] EDGE of The Web Sponsor: SE Ranking [01:04:27] How Can SEO Tools Gauge The Success Of SEO Campaigns In The Context Of SGE? [01:11:52] The Future Of SEO In The Next 18 Months   Thanks to Our Sponsor! SE Ranking: edgeofthewebradio.com/seranking  Follow Our Panelists  Cindy Krum Julie McCoy Ola King JR Oakes Mordy Oberstein

We have a massive EDGE News Flash here for you folks, as we unpack major changes in the Google Knowledge Graph. Joining the flash is a great friend of the show, author and founder of Kalicube, Jason Barnard. Google's Knowledge Vault has set a precedent by experiencing a remarkable 30% growth in just four days, specifically with 'person' entity types which have grown by threefold. Many alterations have been made to Google's ways of comprehending writers, which raises some to question if this is a ploy to identify and combat AI-generated content. Google has taken the training wheels off, tune in as we evaluate all these key changes in the SEO realm on this special EDGE Flash on The EDGE of The Web! Key Segments: [00:00:20] Introducing Jason Barnard [00:01:38] Major Changes in the Google Knowledge Graph [00:03:42] Knowledge Vault vs. Knowledge Panels [00:07:14] Google's New ‘Understanding' Of People [00:12:26] Change in Search Quality Raters Guidelines [00:20:20] EDGE of the Web Title Sponsor: Site Strategics [00:21:27] Is This a Tactic By Google To Identify AI Content?  [00:29:21] What's The Next Major Update Going To Target?  [00:30:43] BrightonSEO Sweepstakes Thanks to Our Sponsors! Site Strategics: http://edgeofthewebradio.com/site   Follow Our Guest Twitter: @Jasonmbarnard LinkedIn: Jason Barnard

Jason is also known as the Brand SERP guy, the one that helps individuals and companies work on their Brands in Search Results. He is the CEO at Kalicube, a great tool that helps you manage your SERPS. We talked about how you as a company with a known brand, or you as an individual, that is a brand, need to work on how you are represented in the Google Knowledge Graph.

In Folge 39 berichtet SEO-Experte Niklas Büllesbach von seinen Erfahrungen im Suchmaschinen-Umfeld. Natürlich gibt Niklas einige Tipps zur Optimierung der eigenen Seite, darüber hinaus werfen wir aber auch einen Blick auf die Funktionsweise von Suchmaschinen. Dabei starten wir bei AltaVista, besprechen die ersten On-Page (TF/IDF) und Off-Page (PageRank) Optimierungen und schließen bei aktuellen Entwicklungen sie Semantischer Suche, hochdimensionale A/B-Tests, Tools wie Google-Search-Console (ehemals WebMaster-Tools) und den Core Web-Vitals sowie Best Practices für sensible Bereiche: Expertise, Authority, Trust. Darüber hinaus skizzieren wir die typsichen Verarbeitungsschritte einer Suchmaschine: Crawling: Der GoogleBot besucht eine Homepage und hangelt sich von hier in die verschiedenen Bereiche der Website. Bei großen Online-Portalen wie etwa Nachrichtenseiten kann das managen des sogenannten Crawl-Budgets eine Herausforderung darstellen.Rendering: Google Caffeein rendert die gecrawlte Websitenstruktur, um anschließend revelante Inhalte zu extrahieren. Bei nicht serverseitige gerenderten Websiten wie etwa Single-Page Applikations kann dies insbesondere auf Grund des zeitlichen Versatzes eine komplizierte Angelegenheit darstellen.Indexing: Die gespeicherten Inhalte werden in Shards unterteilt und auf verschiedene Daten-Center verteilt, die nach URL-Keywords (Reverse-Index) strukturiert werden.Ranking: Die Reihenfolge der zurückgelieferten Suche wird gerade in den letzten Jahren immer mehr durch semantische Suche bestimmt und mittels Neuronaler Netze und dem Google Knowledge Graph bestimmt.

Wie eng gehören Markenbekanntheit und SEO-Rankings zusammen? Anscheinend vertraut Google bekannteren Marken nämlich mehr. Warum das so ist, wieso man sich als Unternehmen unbedingt thematisch positionieren sollte und wie man eine starke, digitale Marke aufbaut, besprechen Olaf Kopp und Jennifer Lapp in der heutigen Digital Helpdesk-Folge. Themen: [01:23] Der Grund für Olafs Interesse an SEO und Markenbildung [05:54] Der Unterschied zwischen Autorität und Marke [12:08] Warum die thematische Positionierung so wichtig ist [15:20] Recap Google Core Updates im Sommer [20:53] Muss ich thematisch nischiger sein? [32:05] Wie baue ich eine starke digitale Marke auf? In der Show erwähnt: Entitäten-basierte Suche: So funktioniert der Google Knowledge Graph - websiteboosting.com  Semantische Suche - So funktioniert Google in 2021 und zukünftig | Webinar Aufgesang  E-A-T – Warum ist es für die SEO der Banken-, Versicherungs- und Healthcare-Branche so wichtig? Feedback? Gerne an podcast-dach@hubspot.com  Mehr über uns unter: https://www.hubspot.de/podcasts/the-digital-helpdesk

En la medida que los algoritmos de indexación y la tecnología avanza, un nuevo jugador entra a participar en el trono al lado del contenido (el rey no esta solo). Hablamos del contexto, un elemento que genera relaciones más estrechas dentro de las búsquedas y que da un norte más claro al momento de responder la pregunta de un usuario. ¿Qué podemos hacer para crear un contexto ideal? Google Knowlegde graph es una de las respuestas y en este episodio hablamos con Blas sobre varias cosas que se pueden gestionar sin necesidad de ser un experto en SEO. Bienvenidos a SEO Bites

In this episode, you will hear Mark Williams-Cook talking about: Google Knowledge Graph authors: How Google is taking further steps in combining author and article data within its Knowledge Graph and showing rich results. New link reclamation tactics: Good this bold new tactic work for you? How GSC reports impressions: Interesting tests showing how you don't need your site to appear in the search results to get impressions. You can get the full transcription and links to resources at https://search.withcandour.co.uk

Wikipedia, Google and social networks transformed the way of knoweldge aggregation and spread - but can we make all of humanty's knoweldge machine-readable? Are Knoweldge Graphs enough to achieve that? What technological and social challenges come with Knoweldge democratization?Inspiring and thought provoking conversation with Denny Vrandečić, Head of Special projects at Wikimedia, former Google Knowledge Graph ontologist and Founder of Croatian Wikipedia.

This week on the show, our guests Anu Srivastava and Sudheera Vanguri talk about Document AI with hosts Stephanie Wong and Dale Markowitz. Document AI uses artificial intelligence to improve the way businesses create and manage things like paystubs, tax forms, contracts, and virtually any other business document. Data normally stored on paper can be parsed, enriched, and structured, then stored securely with the use of Document AI. Data becomes more accessible and more manageable. Our guests go on to describe the process of using this powerful tool and instances where developers and enterprise companies could benefit. We talk about Lending DocAI and Procurement DocAI and how offerings like Google Vision and Knowledge Graph enhance these powerful tools. Users of Document AI can take advantage of these tools as well as bring their own expertise to create custom models. Later, we learn about the developer experience when using the Document AI Platform. Our guests talk specifically about the use of Knowledge Graph and how the advanced search capabilities allow Document AI users to collect data from myriad sources, filling in missing information and enhancing the search with other useful data to make your results more usable. To demonstrate the use of the platform and integrated Google AI tools, we hear about the real-world examples of Workday and Mr. Cooper and their document processing and model training. Sudheera Vanguri Sudheera Vanguri is the head of Product Management at Google Cloud Document AI. Anu Srivastava Anu Srivastava is an Applied AI Engineer for ML on Google Cloud. Before that, she was a software engineer in Android Google Cloud Infrastructure. Cool things of the week A handy new Google Cloud, AWS, and Azure product map blog Compare AWS and Azure services to Google Cloud docs Google Cloud and Seagate: Transforming hard-disk drive maintenance with predictive ML blog Interview Document AI site BigQuery site Lending DocAI site Procurement DocAI site Cloud Natural Language site Google Vision AI site Google Knowledge Graph site Cloud Translation site Workday site Mr. Cooper site AODocs site Processors overview site Python Codelab site Getting started with the Document AI platform video What’s something cool you’re working on? We’ve been working hard on Google I/O.

Du lernst, was der Google Knowledge Graph ist, ob er wirklich so wichtig ist und wie du deinen Content dafür optimierst.

This week, Jon Foust and Michelle Casbon bring you another fascinating interview from our time at Next! Michelle and special guest Amanda were able to catch up with Paco Nathan of Derwen AI to talk about his experience at Next and learn what Derwen is doing to advance AI. Paco and Derwen have been working extensively on ways developer relations can be enhanced by machine learning. Along with O’Reilly Media, Derwen just completed three surveys, called ABC (AI, Big Data, and Cloud), to look at the adoption of AI and the cloud around the world. The particular interest in these studies is a comparison between countries who have been using AI, Big Data, and Cloud for years and countries who are just beginning to get involved. One of the most interesting things they learned is how much budget companies are allocating to machine learning projects. They also noticed that more and more large enterprises are moving, at least partially, to the cloud. One of the challenges Paco noticed was the difference between machine learning projects in testing versus how they act once they go live. Here, developers come across bias, ethical, and safety issues. Good data governance polices can help minimize these problems. Developing good data governance policies is complex, especially with security issues, but it’s an important conversation to have. In the process of computing the survey data, Paco discovered many big companies spend a lot of time with this issue and even employ checklists of requirements before projects can be made live. In his research, Paco also discovered that about 54% of companies are non-starters. Usually, their problems stem from tech debt and issues with company personnel who do not recognize the need for machine learning. The companies working toward integrating machine learning tend to have issues finding good staff. Berkeley is working to solve this problem by requiring data science classes of all students. But as Paco says, data science is a team sport that works well with a team of people from different disciplines. Paco is an advocate of mentoring, to help the next generation of data scientists learn and grow, and of unbundling corporate decision making to help advance AI. Amanda, Michelle, and Paco wrap up their discussion with a look toward how to change ML biases. People tend to blame ML for bias outcomes, but models are subject to data we feed in. Humans have to make decisions to work around that by looking at things from a different perspective and taking steps to avoid as much bias as we can. ML and humans can work together to find these biases and help remove them. Paco Nathan Paco Nathan is the Managing Parter at Derwen. He has 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Paco is also the Co-chair Rev. Advisor for Amplify Partners, Recognai, Primer AI, and Data Spartan. He was formerly the Director of Community Evangelism for Databricks and Apache Spark. Cool things of the week CERN recreated the Higgs discovery on GCP video To discover the Higgs yourself, check out the CERN open data portal site Fun facts from Michelle’s visit: Seven total, four main experiments ATLAS (largest, general-purpose) site CMS (prettiest, general-purpose) site ALICE (heavy-ion) site LHCb (interactions of b-hadrons, matter/antimatter asymmetry) site The French/Swiss border runs across the CERN property Streetview of CERN control center site CERN is the birthplace of the web Where the protons come from site Watch Particle Fever movie Interview Derwen, Inc. site Derwen, Inc. Blog blog Cloud Programming Simplified: A Berkeley View on Serverless Computing paper Apache Spark site Google Cloud Storage site Datastore site Kubeflow site Quicksilver site O’Reilly Media site Google Knowledge Graph site Jupyter site JupyterCon site The Economics of Artificial Intelligence site “Why Do Businesses Fail At Machine Learning?” by Cassie Kozyrkov video The Gutenberg Galaxy site Programmed Inequality site Question of the week Stadia Connect occurred last Thursday. What are some of the biggest announcements that came out of it? Where can you find us next? Jon is in New York for Games for Change. Michelle and Mark Mirchandani are back in San Francisco. Brian & Aja are at home in Seattle. Gabi is in Brazil. Sound Effect Attribution “Crowd laugh.wav” by tom_woysky of Freesound.org

In this episode of The Smart City Podcast, I interviewed the Director of Harvest Digital Planning Brian Ashton. Brian is passionate about creating and geospatial technology and combining these to things to help government to better engage with the community. Brian and I discuss many things mainly focused around using digital and spatial technology to better engage and integrate across all disciplines, government and community. Brian has some really big and great ideas in this space, some of these include breaking down data silos for better integration, Google Knowledge Graph for planning, testing Smart Tech in real places and thick data to bring social meaning to big data.Find the full show notes and links at www.thesmartcitypodcast.com Connect with Brian via LinkedIn or Harvest websiteConnect with me via email: zoe@thesmartcitypodcast.com, Twitter or Facebook @smartcitypod

The Google Knowledge Graph is a system that was launched in May 2012. It understands facts about people, places and things and how these entities are all connected. The Knowledge Graph is used both behind-the-scenes to help Google improve its search relevancy and also to present Knowledge Graph boxes within its search results that provide us consumers with direct answers. You and I know the Knowledge Graph as the little box/panel or card that appears on the right hand side of the Results page or at the top of the results below the AMP carousel on mobile devices. So, as small business DIY SEOers, how can we use the Knowledge Graph to our advantage? Today, I’m talking to legendary SEO guru Bill Slawski to get the lowdown on all things Knowledge Graph. Tune in to learn: What is the Google Knowledge Graph? What does ‘things not strings’ mean? Why the Knowledge Graph is important for your business How often do Knowledge Graph cards show up The anatomy of the Knowledge Graph card How to enhance your results Does the Knowledge Graph actually damage our site? https://www.therecipeforseosuccess.com/knowledge-graph-101-make-work-business

Large knowledge graphs increasingly add great value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. These applications include Google search, Bing search, IBM’s Watson, but also smart mobile assistants as Apple’s Siri, Google Now or Microsoft’s Cortana. Popular knowledge graphs like DBpedia, YAGO or Freebase store a broad range of facts about the world, to a large extent derived from Wikipedia, currently the biggest web encyclopedia. In addition to these freely accessible open knowledge graphs, commercial ones have also evolved including the well-known Google Knowledge Graph or Microsoft’s Satori. Since incompleteness and veracity of knowledge graphs are known problems, the statistical modeling of knowledge graphs has increasingly gained attention in recent years. Some of the leading approaches are based on latent variable models which show both excellent predictive performance and scalability. Latent variable models learn embedding representations of domain entities and relations (representation learning). From these embeddings, priors for every possible fact in the knowledge graph are generated which can be exploited for data cleansing, completion or as prior knowledge to support triple extraction from unstructured textual data as successfully demonstrated by Google’s Knowledge-Vault project. However, large knowledge graphs impose constraints on the complexity of the latent embeddings learned by these models. For graphs with millions of entities and thousands of relation-types, latent variable models are required to exploit low dimensional embeddings for entities and relation-types to be tractable when applied to these graphs. The work described in this thesis extends the application of latent variable models for large knowledge graphs in three important dimensions. First, it is shown how the integration of ontological constraints on the domain and range of relation-types enables latent variable models to exploit latent embeddings of reduced complexity for modeling large knowledge graphs. The integration of this prior knowledge into the models leads to a substantial increase both in predictive performance and scalability with improvements of up to 77% in link-prediction tasks. Since manually designed domain and range constraints can be absent or fuzzy, we also propose and study an alternative approach based on a local closed-world assumption, which derives domain and range constraints from observed data without the need of prior knowledge extracted from the curated schema of the knowledge graph. We show that such an approach also leads to similar significant improvements in modeling quality. Further, we demonstrate that these two types of domain and range constraints are of general value to latent variable models by integrating and evaluating them on the current state of the art of latent variable models represented by RESCAL, Translational Embedding, and the neural network approach used by the recently proposed Google Knowledge Vault system. In the second part of the thesis it is shown that the just mentioned three approaches all perform well, but do not share many commonalities in the way they model knowledge graphs. These differences can be exploited in ensemble solutions which improve the predictive performance even further. The third part of the thesis concerns the efficient querying of the statistically modeled knowledge graphs. This thesis interprets statistically modeled knowledge graphs as probabilistic databases, where the latent variable models define a probability distribution for triples. From this perspective, link-prediction is equivalent to querying ground triples which is a standard functionality of the latent variable models. For more complex querying that involves e.g. joins and projections, the theory on probabilistic databases provides evaluation rules. In this thesis it is shown how the intrinsic features of latent variable models can be combined with the theory of probabilistic databases to realize efficient probabilistic querying of the modeled graphs.

Ian Buck and Ian Decker discuss a Steam death threat, some new GTX 900 series features, a new Titanfall game mode, the next Living World installment in Guild Wars 2, the new Google Knowledge Graph showing game information, the Golden Joystick results, and Buck's review of Badland.

Willkommen zur 5. Ausgabe des IT-Kellers. Besprochen wurde: E-Zigaretten (1,2-Propandiol, nikoBlue), Pebble und Google My Tracks (PebbleMyTracks auf GitHub), Wearables (Jawbone), Michael Bay und Samsung, Parallella Board, Nocard, legendäre Schlacht auf EVE Online (Screenshot), Android Device Manager App (Play Store), 3D Printing (Hubs), Google Knowledge Graph, Wikidata, Daisy AI Podcast, und vieles mehr. Die lustige Nummer am Ende war "Blitzo" von Pirron und Knapp. Gäste: Thomas und Ulrich

Daron and Chris review Google Knowledge Graph and Yahoo Axis. The Knowledge Graph enables you to search for things, people or places that Google knows about and instantly get information thats relevant to your query. Yahoo Axis lets users sync their search experiences across computers, phones and tablets.

Daron and Chris review Google Knowledge Graph and Yahoo Axis. The Knowledge Graph enables you to search for things, people or places that Google knows about and instantly get information thats relevant to your query. Yahoo Axis lets users sync their search experiences across computers, phones and tablets.

Facebook på børs (02:50), Mark har giftet seg (11:10), Google Knowledge Graph (15:40), Einar har testet Diablo 3 (28:10), Chrome størst i verden (43:15), YouTube fyller 7 år (50:45), Medvirkende: Einar Holten (@TCi82) og Jan Espen Pedersen (@Jan_Espen), Gikk direkte: 21 mai 2012 kl 21:30-22:30