{"id":6671,"date":"2024-09-26T11:50:06","date_gmt":"2024-09-26T15:50:06","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=6671"},"modified":"2024-09-26T12:34:54","modified_gmt":"2024-09-26T16:34:54","slug":"understanding-how-new-knowledge-graphs-work","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/understanding-how-new-knowledge-graphs-work\/","title":{"rendered":"Understanding How New Knowledge Graphs Work"},"content":{"rendered":"

\"\"<\/p>\n

DataStax’s Davor Bonaci offers insight on understanding how new knowledge graphs work. <\/strong><\/em>This article originally appeared on Solutions Review’s Insight Jam<\/a>, an enterprise IT community enabling the human conversation on AI.<\/strong><\/em><\/p>\n

Knowledge graphs help us see how different pieces of information are related and help to organize and extract information from large amounts of content. Traditionally, knowledge graphs have been very selective, focusing on extracting specific entities like people, companies, or places and how they\u2019re connected. However, selecting the details to build graphs is necessarily lossy and can be time-consuming, expensive, and prone to errors.<\/p>\n

Imagine you wanted to create a curated map of everything you know about a specific area that makes it easy for others to navigate the space without getting lost. Think back to the days of folding paper maps and atlases before Google Maps. To do this with a traditional knowledge graph, you would have to painstakingly identify every important concept, like who works at what company, and every relationship between those concepts, like who manages whom.<\/p>\n

You\u2019d also need to have an expert on hand to help decide what\u2019s important enough to include. In both cases, maps and graphs will be used to analyze and visualize for end users. So, including unnecessary information has negative consequences: cluttered data, slower performance, increased complexity, diluted insights, higher costs, the potential for misinformation, maintenance challenges, and confusion for users.<\/p>\n

The process doesn\u2019t stop there. If you decide to change how you\u2019re organizing this information, you have to go back and reprocess everything. This makes traditional knowledge graphs very challenging to manage, especially when you\u2019re dealing with a lot of information.<\/p>\n

A New Approach: Content-Centric Knowledge Graphs<\/strong><\/h3>\n

Just like when the user needs and technology evolved bringing about google maps allowing us to interact with maps with natural language, filters, modes and more. A new approach is now needed because of the benefits and needs of generative AI applications and the pros and cons of using large language models (LLMs). Here it\u2019s important to provide the correct and complete context for the LLM to give a relevant answer without hallucinating.<\/p>\n

Instead of focusing on these selective details from the start, there\u2019s a new approach that\u2019s much easier to handle and retains all of the content you need for use with LLMs. This method focuses on what\u2019s called a “content-centric knowledge graph.” Here\u2019s how it works:<\/p>\n

    \n
  1. Chunking Content: First, you break down the content into chunks\u2014these could be paragraphs, sections of a document, images, or tables. These chunks become the building blocks of the graph.<\/li>\n
  2. Preserving Original Content: Unlike the traditional method, where you\u2019d have to decide what\u2019s important upfront, this approach keeps all the original content intact. This means you don\u2019t lose any information, and it\u2019s easier to adapt if your needs change later on.<\/li>\n
  3. Automatic Linking: The graph is built by automatically identifying relationships between chunks based on things like hyperlinks, keywords, or other metadata. For example, if one chunk of text mentions another chunk, a link is automatically created between them.<\/li>\n<\/ol>\n

    Why This Matters<\/strong><\/h3>\n

    This new method is less work because you don\u2019t need to be an expert to build the graph, and it leverages different technology: starting with vector search means you can extract semantically similar chunks without needing to reduce down to entities walking the graph that return related relevant information. It\u2019s also scalable because the backing technology is built for real-time indexing retrieval, meaning it can handle large amounts of information without a problem. When you need to find answers, this graph helps by combining the benefits of traditional knowledge graphs and modern search techniques. It retrieves relevant content quickly, leading to better and more accurate results.<\/p>\n

    Generative AI is changing the way we think about organizing and retrieving information. Content-centric knowledge graphs make it easier to handle large datasets, reduce the need for expert input, and ensure that no information is lost in the process. As this technology continues to evolve, it promises to make searching for and using information for generative AI more intuitive and effective than ever before.<\/p>\n","protected":false},"excerpt":{"rendered":"

    DataStax’s Davor Bonaci offers insight on understanding how new knowledge graphs work. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI. Knowledge graphs help us see how different pieces of information are related and help to organize and extract information from large amounts of content. […]<\/p>\n","protected":false},"author":1147,"featured_media":6673,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[355,1449],"acf":[],"yoast_head":"\nUnderstanding How New Knowledge Graphs Work<\/title>\n<meta name=\"description\" content=\"DataStax's Davor Bonaci offers insight on understanding how new knowledge graphs work. 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