We Love SEO is back and this time it is all online ! After a short hiatus, the highly anticipated We Love SEO conference organised by Oncrawl is taking place on December 5th ! Sign up for free to join the 9th edition of We Live SEO which features real-life case studies, roundtable discussions and keynote speeches. Dani Leitner, Crystal Carter and Andrea Moro start the day with an SEO Panel on the Best SEO advice. Then Rejoice Ojiaku presents: Crafting your brand story: A guide to driving e-commerce success Lazarina Stoy takes us through 5 phases of NLP: Theory, practice, and implications for Search Marketers Aftere a lunch break, Nikki Halliwell presents with a case study: <Decoding/> the Language of Devs Then we have a panel on: AI & Search: Where do we stand? Is it important? with Kevin Indig, Andreas (Dre) Voniatis, Corentin Mirande and Syphaïwong Bay Helen P. rounds off the day with: Looking Ahead: SEO Industry Predictions for 2025 https://lnkd.in/d_SikgRz
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Citations alone helps in site rankings? 🤔🤔🤔 The single variable test Data Says YES! Optimised my citation information data with semantics, Data science things like NLP, NLU, IR stuff. So that a search can process the information better. No other backlinks or no content updates. Only few Technical SEO fixes. It's a roofing niche, and quite competitive in UK market. Time for this result is 2 weeks (started this month on 10th). Magic happens when we merge the knowledge from different disciplines like, Data sciences, Technical SEO, Thank you Koray Tugberk GUBUR for the interview video in his group, that lead me to testing this new idea. Takeaways : Optimise the initial source of the data, for any website citation information is initial source of what a website is about. Start thinking the ways to optimise from it. The whole ranking a site get better. #SEO #Testing #Ideas #DataScience
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QNAP integrates AI-powered semantic search into your NAS file search experience, moving beyond traditional keyword search! 🔍✨ https://qnap.to/5ujlzq The Qsirch 5.4.0 beta search engine now supports semantic search queries for better image search. Now you can use intuitive and natural language prompts, and Qsirch will interpret your query intent and search context to help you save time in image search with more precise and rapid results. 😉 😉 😉 🔥 Key new features 🔥 ✔️ AI-driven semantic search: Find relevant images using semantic search queries; such as, “a dog drinking water”. ✔️ Explore similar images: Easily find more similar images from search results. ✔️ Narrow down search results efficiently: Work smarter and find images faster with high accuracy. ✔️ File content preview with AI: Users can quickly preview the content in a preview pane, view keyword-relevant paragraphs, or preview a few relevant sentences related to the file subject using an intuitive GUI. 📢 Join Qsirch 5.4.0 semantic search beta test and try all new features > https://qnap.to/5ujlzq #QNAP #NAS #Qsirch #semanticsearch #AI
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Big news for the SEO world! 🌟 We’ve just launched Auto Optimizer—a tool designed to take the headache out of manual SEO tasks. 🎉 We know how challenging it is to keep up with every detail: ➡️ Adding those important NLP terms ➡️ Fixing meta tags like Titles, Descriptions, and URLs ➡️ Finding and filling topic gaps ➡️ Adding internal links to improve your site structure That’s why Auto Optimizer is here—to handle these tasks automatically, so you can focus on creating great content. This is our step toward solving one of the biggest SEO challenges. Check it out and let us know your thoughts! #SEO #AutoOptimizer #Scalenut #SimplifiedSEO
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🚀 I just pushed a new project to GitHub utilizing CrewAI agents designed for Google’s #KnowledgeGraph API 👉 https://lnkd.in/dgXDUgj8 🤔 Did you know that Google's #KnowledgeGraphs are better than the traditional general #GoogleSearch when looking for verified and structured information about well-known people, companies and entities? 🔥 Yes, Knowledge Graphs not only pull data from reputable sources but also continuously update and refine through #MachineLearning and NLP with semantic understanding of queries. This setup uses 2 #CustomTools plus CrewAI's built-in #ScapeWebsiteTool The agents prioritize extracting info about a query from Google's Knowledge Graph blocks and only fall back to the general Google Search if a knowledge graph is not available. SerpApi offers multiple search engines, and had I not been in a weekend mood already I would extend the crew to fall back to other search engines like DuckDuckGo whenever Google fails to provide satisfactory results 🙂 Below is a straightforward implementation of the two custom tools.
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ai agent with 2 tools
🚀 I just pushed a new project to GitHub utilizing CrewAI agents designed for Google’s #KnowledgeGraph API 👉 https://lnkd.in/dgXDUgj8 🤔 Did you know that Google's #KnowledgeGraphs are better than the traditional general #GoogleSearch when looking for verified and structured information about well-known people, companies and entities? 🔥 Yes, Knowledge Graphs not only pull data from reputable sources but also continuously update and refine through #MachineLearning and NLP with semantic understanding of queries. This setup uses 2 #CustomTools plus CrewAI's built-in #ScapeWebsiteTool The agents prioritize extracting info about a query from Google's Knowledge Graph blocks and only fall back to the general Google Search if a knowledge graph is not available. SerpApi offers multiple search engines, and had I not been in a weekend mood already I would extend the crew to fall back to other search engines like DuckDuckGo whenever Google fails to provide satisfactory results 🙂 Below is a straightforward implementation of the two custom tools.
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What is semantic search 🔎 ? Semantic search is a modern technique for search engines like Elastic to understand what you mean when you type something. Instead of just finding documents with the exact words you type, it tries to find documents that match the meaning behind your words. It uses technology like artificial intelligence to do this. 🛠 Here's how it works: First, it turns text into numeric vectors representing your request. Then, it compares these vectors to other numbers that represent documents. It looks for the most similar ones and gives you those as results. "Context" is important in semantic search. That means where you are, what you've searched for before, and even the words around what you're looking for. For instance, if you type "slacks", it knows if you mean pants or trousers. Another important thing is understanding what you want. Semantic search tries to figure out if you're looking for information, trying to buy something, or something else. It then shows you the most fitting results based on what you need. The benefits of semantic search for business are obvious. Semantic search helps understand what customers want, whether it's information, buying something, or just exploring. This understanding can lead to more sales and happier customers, building a better relationship between customers and the brand. 💁♂️ Feel free to reach out if you want to discuss how semantic search can benefit your business! #elasticsearch #elastic #search #ai #semanticsearch #krastysoft
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🚀 Excited to share a recent success story! 🚀 By implementing cutting-edge semantic and NLP SEO techniques and strictly adhering to Google's guidelines, I've transformed my website's traffic from nearly zero to an impressive 18K visitors in just a few months! This remarkable growth followed the latest Google core update. If you're looking to boost your website's performance, understanding and leveraging these SEO strategies is key! #SEOSuccess #DigitalMarketing #GoogleUpdate #NLPSEO #WebsiteTraffic
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SEO Stack's next feature rolling out shortly. Bulk rewrite titles for your sites - select URL + intent and run. We'll also add filters by CTR so you can run in bulk for LOW CTR URLS. We'll be able to make additional recommendations based on SERP features also. Again - as with EVERYTHING - we will continue to refine and improve each feature. We'll scrape content from page - evaluate CTR / NLP and use that to improve title tags to better reflect offering. Built BY SEOs FOR SEOs. #seo #seosaas #seotools
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🔍 Just stumbled upon an insightful read on Search Engine Land about the future of search - think less about sifting through endless results, and more about embarking on a "Choose Your Own Adventure" saga. The key players? LLMs (Large Language Models) vs. Knowledge Graphs. Here’s the scoop: As we move forward, the way we search for information is getting a major makeover. Picture asking a question and then being led through a series of follow-up queries, each tailored to narrow down to exactly what you're seeking. This isn't just about getting answers, it's about journeying through hyper-relevant content that evolves with each query. For us content creators, this shift means our work needs to adapt to these multidimensional searches. We're no longer just aiming to match keywords but to engage users at multiple stages of their search adventures. The challenge? Ensuring our content is versatile enough to be part of these highly personalized journeys. Exciting times ahead! Let's chat about how we can all stay ahead of the curve. #ContentMarketing #SEO #FutureOfSearch #Innovation #TechTrends #AI #SearchEngineOptimization #ContentStrategy #UserExperience #MachineLearning #FutureTrends #MarketingStrategy For those curious about diving deeper into this evolution, I highly recommend giving this article a read, it's very insightful : [How search generative experience works and why retrieval-augmented generation is our future](https://lnkd.in/em87NmQ4)
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Just shipped a deeeeeep dive on building a member retrieval system using semantic search 🤿 It won a build bounty competition at Build Club 🥰 If you just care about how the semantic retrieval with embeddings works, this post covers just that 💖 🔎 Semantic search is where the search engine knows what you’re looking for, even if you don’t say the exact words. Embeddings are numeric representations of words, phrases, documents etc that capture semantics, like grammatical info, conceptual properties and relationships between words and phrases like synonyms and associations etc. Imagine you have the following search query: 😸 Feline = [0.88, 0.08] And the following word embeddings: 🚂 Train = [0.1, 0.9…] 🐈 Cat = [0.9, 0.1…] 🐶 Dog = [0.08, 0.15…] 🦁 Lion = [0.85, 0.05…] Using a semantic similarity algorithm designed for measuring how closely embeddings are related (cosine similarity is the most typical algorithm), we would get back the following results: 🐈 Cat = [0.9, 0.1…] 🦁 Lion = [0.85, 0.05…] 🐶 Dog = [0.08, 0.15…] 🚂 Train = [0.1, 0.9…] Cat, and Lion are closer in semantic meaning to “Feline” than Dog or Train. This process of organising data based on how similar it is to the search query is called Ranking. Things to watch out for: 🙈 Semantic search will always return results even if there are no relevant results. So you need to implement a check that there are relevant results first. 🪂 You will also need to implement an ejection step so that if there are relevant results, only relevant results are returned. In the example above, we only want “Cat” and “Lion”, not “Dog” and “Train”. Otherwise your searchers can scroll right down to irrelevant results which can be confusing. 💌 Read the deep dive here: https://lnkd.in/g_hzcnZt #ai #genai #generativeai #rag #retrievalaugmentedgeneration #chatbot #embeddings #search #semanticsearch
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