The Web Visions的动态

?? Google’s New Search Engine Is Bad News for the Web Economy: Do we trust Gartner or Google? https://lnkd.in/eVeznu5X ??What You See In The Dark: Rudy Golbert emerges from the dark. https://lnkd.in/gjdnE-Di ??The Complicated Ethics of Rare Book Collecting: Ask us about our history of book collecting. https://lnkd.in/gYR6ehi6

Google understands words used in searches as representations of real-world entities, not just random strings of characters. Knowledge Graph creates informational links about real-world entities like people, books, or movies. Understanding relationships between things, like artists in the Renaissance or Nobel Prize-winning women, helps Google deliver more precise search results. Features like the Knowledge Graph panel alongside web results enhance search by integrating relevant data found across the web. Google uses collective human knowledge through search queries to enrich its database and improve user search experiences. Searching for specific artists or historical figures brings direct information and related topics, expanding understanding of historical eras like the Renaissance. Google's transition from an information engine to a knowledge engine marks a significant evolution in search technology. As the Knowledge Graph grows richer, Google aims to better understand user search queries and connect diverse web information intelligently. Want to understand Google Knowledge Graph in more detail so watch the below video and see how actually Google Knowledge Graph work. https://lnkd.in/d8HmGP_r #semanticseo #seo #digitalmarketing #topicalmap #onpageseo

Introducing the Result Assessment Tool or in short, "RAT" ??: an easy and effective solution for search engine research. ?????? Despite the significant need for research on search engines in the field of information science, obtaining official access to search engine data can present a challenging task. Data gathered for research on search engines is often done manually using various software tools, resulting in a significant workload. RAT offers a quicker, more adaptable, and precise solution to assist researchers in their search engine studies. RAT helps create research studies on search engines, including relevance assessment, content analysis, and overlap analysis. It can scrape data from various search engines and allows manual and automated evaluations. Test RAT ??: https://buff.ly/3QajVvU Join our community meeting in September: https://buff.ly/3vGrMuv Learn more about our research group Search Studies: https://buff.ly/4aFbnW5 #searchstudies #searchresearch #searchengine #RATSoftware #rat #ratcommunitymeeting #informationscience #searchenginedata #datescience #retrievalstudy #researchsoftware #google

I’ve started to test the nuggets of ranking magic gleaned from the Google algorithm leak…and the results are good. This image is a vectarized version of a side project of mine called The Flask and Barrel. The idea is simple. Google relies on embeddings to understand the web…so I am going to create a vector embedding for my own site. What are vector embeddings? It’s just a 3d representation of semantically similar content. How am I initially using my vector embeddings? I want to figure out which articles are most related so I can determine internal link targets. In addition to that, I want to visually see that I am building a dense cluster of content rather than a bunch of distant content nodes. I don’t want to publish results until I get more data..but I do have a tip. You should incorporate a knowledge of how algorithms “understand” content. If you did this prior to the Google Algorithm leak, you would have basically covered about 75% of the info found in the algorithm leak.

We're going to talk about how to align your page's top embedding cluster! At the end of this, you'll be able to do this for any page you want with our new free tool!

?? #SEOTip the supposed 14,000 ranking signals are not ranking signals -- THEY ARE THE NAMES OF API CALLS. Also the entire set of files isn't just about supposed search they have things like how to do an app notification or email or a click to call phone call. So when someone says 14,000 signals they didn't actually read all the files because they know it's not only about supposed search features, which again don't necessarily have anything to do with the search engine and what they publish to the public. We literally do not know what they are except that they are API calls used internally at Google. We don't know what system they're used for we don't know if they used only internally, they could be used for testing etc YET now I'm seeing people create searchable databases WITH WEIGHTS WTF? These are not ranking signals. I'm going to repeat again THESE ARE NOT RANKING SIGNALS THEY ARE API CALL NAMES WITH THEIR DEFINITIONS. Just ignore these and move on unless you're just really interested in how Google defines these names in the API calls. this is not going to help you do better in search and it's a waste of your time if you're not just doing it for research. Like some of the definitions do have interesting information like how they measure geolocation - now that could apply across the number of products including just your phone, but it is an interesting definition. But for those who think these are Google Secret sauce to the search algorithms I'll ask you to ask yourself one simple question. If these are truly deep insights into Google's ranking signals do you think they'd still be up there for people to look at? Nothing these have been up there for 2 years. They're not new. They weren't published by some internal leaker they were an accidental automatic publish from Google themselves and they are still there - well as of Friday. So much garbage out there about what these could be and who knows maybe we'll find out they do something to do a search later but they're not showing you anything in any comprehensive way that you can put together to make analysis from. Oh and the term search features is also used in terms of machine learning so could even just be that. Video is of the "leaker" just meaning person who found them on GitHub. https://lnkd.in/ghhz5KPs

Your Google Search results are about to look very different. Here are 4 reasons why https://lnkd.in/eMqPGuTb #LatestLaptopReview

In 2018, I wrote an article for the then-Distilled blog about how the top few results for higher-volume terms seemed to exhibit different ranking behavior to the rest of search. “The two-tiered SERP,” I called it, and hypothesized that this could be caused by the availability of click data. Fast forward to 2024, and there's this concept of a "navboost query" in the Google leak, and nobody seems to have a concrete definition of it. As you might guess, I have a theory, so I wanted to see how it holds up. You might be forgiven for saying - even if you're right, this is by definition old hat. Not new. That's somewhat true, but also unfair. Being very confident rather than just speculative about this kind of foundational understanding allows us to build a deeper understanding, and tools, and recommendations, and talk about them in a different way. If you're interested - https://lnkd.in/eEBVWNMe

Delighted to share that DFG has extended its support for our work on the Result Assessment Tool (RAT) for three more years! RAT is a toolkit designed to streamline the study of search engine data. We're excited to build a more stable and sustainable solution! ?? ?? #RATSoftware #searchstudies #searchengine #rat #ratcommunitymeeting #informationscience #searchenginedata #datascience #researchsoftware #google

Google search engine work. How to Crawling, Indexing and ranking data on Google search engines .