[Music] So, as you come in, we have here a server set up with everything you'll need. If you want to follow along, you should have gotten a post-it note. If you don't, just raise your hand and my colleague Alex over here will come find you and we'll provide you with one.Uh, basically what you're going to do is you're just going to go, if you have a number 160 or below, you go to this link here, the QR code on top as well. Um, and if you have a number that's 2011 or above, you go to the second link or the QR ...

[Music] I'm going to go over today graph rag particularly dealing with multiple data sources. Uh so both unstructured and structured data sources and kind of why you would want to ever do that in the first place even. So I prepared uh some notebooks here.I was going to make slides, but then I thought it would just be easier to walk through some of what this looks like in practice. Um, so there's a link here, and I can share it with you at the booth later, um, if you have follow-up questions. But basically, ...

Core Insights - Knowledge graphs face significant challenges in scalability, data quality, completeness, and ethical considerations, which must be carefully managed to realize their potential benefits [1][13]. Scalability and Performance - A major challenge is scaling knowledge graphs to billions of nodes/edges while maintaining performance for complex queries and updates [1]. - If performance is not considered during the design of knowledge graph solutions, query responses may slow down as data grows [2]. Data Quality and Consistency - The utility of knowledge graphs heavily relies on data quality, which can be difficult to ensure due to aggregation from multiple sources with varying reliability [3]. - Inconsistencies and errors may arise, such as conflicting information about the same entity, making it challenging to maintain a reliable knowledge graph [3]. Completeness - Knowledge graphs often suffer from incompleteness, where not all relevant facts are captured, leading to potential misinterpretations by AI systems [4]. - The closed-world assumption can lead to false negatives if certain information is simply not recorded in the knowledge graph [4]. Complexity of Schema and Ontology Management - Designing an effective schema (ontology) for knowledge graphs is complex, requiring a balance between specificity and flexibility [5][6]. - Overly rigid schemas can hinder the integration of new data sources, while overly loose schemas may lose semantic precision [5]. Integration with Unstructured Data - Many data sources are unstructured or semi-structured, making the extraction and integration of information into knowledge graphs challenging [7]. - Maintaining consistency between unstructured data and knowledge graphs requires careful design and often manual oversight [7]. Handling Dynamic Data - Knowledge graphs struggle with highly dynamic data, such as real-time sensor data, making updates and consistency difficult [8]. - Traditional triple stores are not optimized for streaming updates, complicating the maintenance of knowledge graphs in fast-changing environments [8]. Bias in Knowledge Graphs - Knowledge graphs can reflect and amplify biases present in their source data, leading to unfair decision-making by AI systems [9]. - It is crucial to analyze and mitigate biases in knowledge graphs to ensure fair outcomes [9]. Privacy and Ethical Issues - Knowledge graphs can integrate personal data, raising significant privacy concerns, especially regarding compliance with regulations like GDPR [10]. - Ethical considerations around data combination and re-identification risks must be addressed when constructing knowledge graphs [10]. Tools and Expertise Gap - Organizations often face challenges in finding skilled personnel familiar with knowledge graph technologies, which can hinder project success [11]. - The fragmented technology stack and lack of standardization compared to relational databases complicate the recruitment of qualified engineers [11]. Maintenance and Evolution - Continuous maintenance is essential for knowledge graphs to remain relevant, requiring resources to update and manage outdated information [12]. - Knowledge governance planning is critical but often underestimated, impacting the long-term value of knowledge graphs [12]. Integration with Legacy Systems - Integrating knowledge graphs with existing IT systems can be challenging due to performance mismatches and data model incompatibilities [12]. - Without effective integration tools or training, knowledge graphs may not be fully utilized within organizational workflows [12].

Core Points - The article discusses the architecture and technology choices for social applications, emphasizing the importance of selecting the right frameworks and services for development [5][8][9]. Group 1: Frontend Development - The frontend of a social app consists of mobile (iOS/Android) and web applications, utilizing frameworks like React.js, Vue.js, and Angular for single-page applications [3][5]. - Mobile app development can be native (using Swift for iOS and Kotlin for Android) or cross-platform (using React Native, Flutter, uni-app, or Taro), each with its own advantages and disadvantages [6][8]. Group 2: Backend Development - The backend handles business logic, data storage, user authentication, and API interfaces, with popular frameworks including Spring Boot for Java, Django for Python, and Express.js for Node.js [9]. - Java is noted for its high performance and stability, making it suitable for large-scale applications, while Python offers rapid development capabilities for smaller projects [9]. Group 3: Database and Storage Solutions - Relational databases like MySQL and PostgreSQL are commonly used for structured data, while NoSQL databases like MongoDB and Redis are preferred for unstructured data and high-speed access [9]. - Object storage services from providers like Alibaba Cloud and Tencent Cloud are essential for managing user-generated content such as images and videos [9]. Group 4: Cloud Services and Compliance - For the Chinese market, compliance with local regulations, including ICP filing and app registration, is crucial, along with the selection of domestic cloud service providers like Alibaba Cloud and Tencent Cloud [8]. - The article highlights the importance of integrating third-party SDKs for functionalities like instant messaging and content moderation, with a focus on local providers [8][9]. Group 5: Development Tools and Technologies - The use of message queues (e.g., Kafka, RabbitMQ) and search engines (e.g., Elasticsearch) is recommended for system decoupling and enhancing user experience through personalized content [9]. - Containerization technologies like Docker and Kubernetes are suggested for efficient application deployment and management [9].