Benefits of Using Knowledge Graphs

Knowledge Graphs (KGs) have long been the unsung heroes behind technologies like search engines and recommendation systems. They store structured relationships between entities, helping us connect the dots in vast amounts of data. But with the rise of LLMs, KGs are evolving from static repositories into dynamic engines that enhance reasoning and contextual understanding. This transformation is gaining significant traction in the research community. Many studies are exploring how integrating KGs with LLMs can unlock new possibilities that neither could achieve alone. Here are a couple of notable examples: • 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Researchers introduced a framework called 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐊𝐆𝐋𝐀). By integrating knowledge graphs into language agents, KGLA significantly improved the relevance of recommendations. It does this by understanding the relationships between different entities in the knowledge graph, which allows it to capture subtle user preferences that traditional models might miss. For example, if a user has shown interest in Italian cooking recipes, the KGLA can navigate the knowledge graph to find connections between Italian cuisine, regional ingredients, famous chefs, and cooking techniques. It then uses this information to recommend content that aligns closely with the user’s deeper interests, such as recipes from a specific region in Italy or cooking classes by renowned Italian chefs. This leads to more personalized and meaningful suggestions, enhancing user engagement and satisfaction. (See here: https://lnkd.in/e96EtwKA) • 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠: Another study introduced the 𝐊𝐆-𝐈𝐂𝐋 𝐦𝐨𝐝𝐞𝐥, which enhances real-time reasoning in language models by leveraging knowledge graphs. The model creates “prompt graphs” centered around user queries, providing context by mapping relationships between entities related to the query. Imagine a customer support scenario where a user asks about “troubleshooting connectivity issues on my device.” The KG-ICL model uses the knowledge graph to understand that “connectivity issues” could involve Wi-Fi, Bluetooth, or cellular data, and “device” could refer to various models of phones or tablets. By accessing related information in the knowledge graph, the model can ask clarifying questions or provide precise solutions tailored to the specific device and issue. This results in more accurate and relevant responses in real time, improving the customer experience. (See here: https://lnkd.in/ethKNm92) By combining structured knowledge with advanced language understanding, we’re moving toward AI systems that can reason in a more sophesticated way and handle complex, dynamic tasks across various domains. How do you think the combination of KGs and LLMs is going to influence your business?

AI Without Context Fails. Graphs Provide the Missing Piece. Large Language Models (LLMs) are powerful but not perfect. They often miss the mark when handling domain-specific data. 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗚𝗿𝗮𝗽𝗵𝘀) fixes this by merging LLMs with knowledge graphs–adding context, structure, and trust. It’s the secret to building AI that delivers meaningful, actionable insights. How so? Instead of relying only on text chunk searches, it uses graph queries to pull relevant, connected data. This creates smarter AI by tapping into relationships between entities. For example: ❓𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗔𝗻𝘀𝘄𝗲𝗿𝗶𝗻𝗴: GraphRAG goes beyond keyword searches. It understands relationships between products, components, and customers to offer personalized solutions, not just answers. 📖 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: Uncover patterns and trends hidden in data. Competitive analysis, risk detection, and market insights become easier with graphs at the core. 🗣️𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: Every result comes with an audit trail, fostering trust and transparency. 👕𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀: Deliver hyper-personal recommendations by linking products, customer behavior, and browsing history. This goes way beyond “similar items” in a product catalog. 🔎𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗦𝗲𝗮𝗿𝗰𝗵: GraphRAG refines queries based on past actions and preferences, ensuring relevant results every time. Building knowledge graphs takes expertise and ongoing maintenance. There are a variety of LLM-based tools making this easier, but human oversight is still essential. Quality data is also critical. Inconsistent or messy data weakens GraphRAG’s effectiveness, just like any other AI or data science project. GraphRAG is reshaping how we use AI in real-world scenarios. It merges the strengths of LLMs with the structure of graphs for better accuracy, context, and transparency. 💬 Have you explored using GraphRAG to enhance your GenIA project? Share your experience in the comments. ♻️ Know someone struggling with a GenAI project? Share this post to help them out. 🔔 Follow me, Daniel Bukowski, for daily insights about building with connected data.

TL;DR: There has been a dramatic uptick in interest in Knowledge Graphs (KGs). Combined with LLMs, KGs can provide better insights into organizational data while reducing or even eliminating hallucinations just like some ideas in 𝗡𝗲𝘂𝗿𝗼-𝗦𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗔𝗜. A long time ago I wrote about how Symbolic AI and Neural AI will come together to unlock new value while lowering enterprise risk. (https://bit.ly/3WZQ11q). We are definitely headed down that path with some interesting startups like Elemental Cognition (https://lnkd.in/eFUhFYEZ) and Amazon Web Services (AWS) using symbolic techniques for security scanning of LLM generated code in Q Developer (https://lnkd.in/ecJTSSaS). Another variant albeit not Neuro-Symbolic AI is the 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗞𝗚𝘀 𝗮𝗻𝗱 𝗟𝗟𝗠𝘀. KGs are inherently symbolic and integrating with LLMs is a no-brainer for specific use cases. A great writeup of the 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 by the excellent Neo4j team (Philip Rathle, Emil Eifrem): https://lnkd.in/ebR6tMD8 which itself builds on some great work by the Microsoft GraphRAG team (https://lnkd.in/enRpA6Y7). Benefits summary: 1. 𝗛𝗶𝗴𝗵𝗲𝗿 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 & More Useful Answers  • A KG combined with an LLM improved accuracy by 3x • LinkedIn showed that KG integrated LLMs outperforms the baseline by 77.6% (https://lnkd.in/eNvvQaeq) 2. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗗𝗮𝘁𝗮 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗙𝗮𝘀𝘁𝗲𝗿 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, and More 𝗔𝗻𝗱 𝗵𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝘄𝗶𝘀𝘁: KGs and ontologies have historically been hard to create and maintain. Turns out you can use LLMs+ to simplify that process!! Great research work here: https://lnkd.in/eTyGjSe5 and actual implementation by the Neo4J team (https://bit.ly/3WIJxmd). If you want to try this using AWS services give it a whirl here: https://go.aws/3T8FK0L 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝘅𝗢𝘀: Consider adding Knowledge Graphs to your enterprise Data and GenAI strategy.