WikiEditorsINC’s Post

The Most Popular Wikipedia Articles in 2023 #WikipediaTrends #PopularArticles #TopWikipedia2023 #MostViewedArticles #WikiHighlights #WikipediaStats #TrendingTopics #WikipediaInsights #InternetTrends #OnlineKnowledge

Is Wikipedia Reliable? The Truth Exposed Discover the reliability of Wikipedia as a source of information. Learn why it's important to use it with caution and find out alternative options for accurate research. #ReliableSources #WikipediaDebate #FactOrFiction #ResearchTips #AlternativeSources #TrustworthyInformation #CriticalThinking #InformationAccuracy #EducationalResources #ResearchWisely

Test your knowledge and win! At Infopedia Experts, we're curious about what's trending on Wikipedia. Here's a question for you: What is the most viewed article on Wikipedia? Let us know your answer in the comments!

𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘅𝘁 𝗶𝗻 𝗺𝘆 𝗚𝗿𝗮𝗽𝗵 𝗦𝗲𝗿𝗶𝗲𝘀 𝗼𝗳 𝗯𝗹𝗼𝗴𝘀? The next articles in my Graph Series of blogs will cover some very interesting and challenging topics. Part 14 of the series will be released anytime between this weekend, 7/09/2024 and 14/09/2024. I've already been discussing the underlying concepts here: 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐺𝑟𝑎𝑝ℎ 𝐸𝑚𝑏𝑒𝑑𝑑𝑖𝑛𝑔𝑠 𝑎𝑛𝑑 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐺𝑟𝑎𝑝ℎ 𝐶𝑜𝑚𝑝𝑙𝑒𝑡𝑖𝑜𝑛: 𝑃𝑎𝑟𝑡 14 𝑜𝑓 𝑚𝑦 𝐺𝑟𝑎𝑝ℎ 𝑆𝑒𝑟𝑖𝑒𝑠 𝑜𝑓 𝐵𝑙𝑜𝑔𝑠 Part 15 will explore another exciting and rapidly evolving topic. Again, I have been already been talking of the essentials around the topic: 𝑇ℎ𝑒 𝑀𝑎𝑟𝑟𝑖𝑎𝑔𝑒 𝑜𝑓 𝑅𝑒𝑡𝑟𝑖𝑒𝑣𝑎𝑙-𝐴𝑢𝑔𝑚𝑒𝑛𝑡𝑒𝑑 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 (𝑅𝐴𝐺𝑠) 𝑤𝑖𝑡ℎ 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐺𝑟𝑎𝑝ℎ𝑠: 𝑃𝑎𝑟𝑡 15 𝑜𝑓 𝑚𝑦 𝐺𝑟𝑎𝑝ℎ 𝑆𝑒𝑟𝑖𝑒𝑠 𝑜𝑓 𝐵𝑙𝑜𝑔𝑠 Stay tuned! #graphs #knowledgegraphs #graphrags

#DailyBlogTip Dig into your blog comments. A reader has hidden a great topic idea in there for you to find and use.

#OpenAlex - comprehensive #aggregated #data on... 📚 250M scholarly publications? 📰 250k sources? 👨🎓 90M authors? 🏫 200k institutions? 🔓 open access +💰APCs? 📊 With full texts as n-grams? 🛠 With an easy-to-use yet powerful #API? 🎂 And all this in the public domain #CC0? ➡ https://openalex.org/ Since I am still and more and more enthusiastic about #OpenAlex #API, there is now an updated version of the #Jupyter #Notebook of the University Library Universität Bern to get started!

Wikipedia isn't just a search engine; it's a fountain of wisdom. 💡 Whether we are seeking answers or crafting new ideas, Wikipedia is my first stop. Thanks, Stephen Colbert, for reminding us where true knowledge resides! 🌐 www.wikpedias.com #WikiPedias #StephenColbert #WikiPediaDay #WikiPediaPage #WikiPediaPageUpdated

Thanks to my friend at the DMAW for the opportunity to write and publish a new blog post. Topic: matching your message to giving level dynamics. Check it out. Link in the first comment below.

Check out my blog post https://wix.to/qLeMnYc #newblogpost

We live in a data-driven world today, and most enterprises are sitting on a goldmine of information (or even raw data). But like with crude oil, this raw data needs refinement to be truly valuable. Traditional databases (Sqls and NoSQLs), while essential, often struggle to capture the intricate relationships and interconnectedness that lie beneath the surface. This is where graph databases shine, offering a powerful tool to unlock the hidden potential of your enterprise's data. So, why knowledge graphs? Imagine your data as a vast network of interconnected nodes, each representing a piece of information. Graph databases excel at modeling these relationships, allowing you to explore and analyze data in ways that traditional databases simply can't. What about the problem of Disparate/Siloed Systems? One of the biggest challenges facing enterprises is data silos. Information is scattered across different systems (departments), making it difficult to get a complete picture (refer to Conway’s law). Graph databases act as a bridge, connecting these disparate systems and creating a unified view of your data. This interoperability enables you to gain deeper insights and make more informed decisions. Some context on Conway’s law: Conway's Law is a principle in software engineering that states: "Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations."   In simpler terms, the way a company is structured and how its teams communicate will directly influence the structure and design of the software systems they create. If a company has silos or communication barriers, the software they produce will likely reflect those silos.   How do Knowledge Graphs support LLMs? For LLms to be useful, they rely on context to provide meaningful results. Graph databases provide the rich context that language models need to understand and respond to queries effectively (Read more about Retrieval Augmented Generation-RAG). By capturing the complex relationships between data points, graph databases help AI systems to be more accurate. Business never sleeps, so how do Knowledge Graphs adapt? As your business evolves, so too will your data. Traditional databases can be rigid, requiring significant effort to accommodate changes. Graph databases, on the other hand, are designed to be flexible. They can easily adapt to new relationships and data structures, ensuring that your technology stack remains relevant as your business grows. Bottom line! Graph databases offer a powerful solution to the challenges of data management in today's enterprises. By connecting disparate systems, providing rich context for AI, and offering unparalleled flexibility, graph databases enable organizations to extract maximum value from their data. If you're looking to understand more on Knowledge Graphs and their practical applications for your enterprise, check out this article by #neo4j #AI