Knowledge Graph Mastery: Build Better AI with Metadata (PHASE 1)

## Overview AskB is revolutionizing how investment professionals interact with financial data, but behind its conversational interface lies a complex challenge: how do AI agents efficiently navigate Bloomberg's vast, interconnected data ecosystem? This challenge puts our group at the forefront of solving real AI optimization problems using knowledge graphs and metadata management - in close collaboration with AskB teams. As Bloomberg's newest AI innovation, AskB processes everything from SEC filings to breaking news, analyst reports to market data. However, current AI agents often struggle with data disambiguation, entity resolution, and understanding complex relationships between financial concepts. This creates opportunities for slower responses, potential inaccuracies, and missed insights that could impact investment decisions. ## Background Bloomberg's data spans decades of financial information across global markets, with millions of entities, relationships, and constantly evolving metadata. AskB's AI agents must quickly understand: - Whether "Apple" refers to Apple Inc. stock, Apple bonds, or Apple executives - How quarterly earnings relate to analyst predictions, peer comparisons, and historical trends - When conflicting news reports should be weighted differently based on source credibility - How regulatory changes impact entire industry sectors and their interconnected companies Currently, these disambiguation and relationship-mapping tasks consume significant computational resources and can introduce latency or accuracy issues. The Metadata CTO Office would like our group of KG practitioners to work with AskB SME to identify critical areas where knowledge graphs could dramatically improve AskB performance, for instance (purely indicative): 1. Entity Resolution & Data Lineage: Faster, more accurate identification of financial entities across data sources 2. Contextual Query Understanding: Better interpretation of user questions through relationship mapping 3. Answer Attribution & Validation: Enhanced citation accuracy and confidence scoring ## What We're Looking For PHASE 1: NOW, for Bloomberg's Metadata experts. Your current challenge is to prepare the best Challenges for AskB engineering teams to work on, later on, in PHASE 2. PHASE 2 & 3: LATER, for Bloomberg's AskB engineers. Your group, the Bloomberg Metadata Office, will act as mentors guiding AskB engineering teams (SMEs) through the solution implementation - solutions that, by requirement, will have to use metadata. Phase 1 Details: Challenge Selection & Design (1-2 weeks, kickstart on 5/22) - Choose one of the three optimization areas or propose your own AskB-related challenge, for which you will then mentor one or more AskB teams. To do this, identify which, exactly, are the most impactful errors to address with metadata/knowledge graphs. That gives SMEs clearer targets when developing solutions - and also develops their skill in identifying what's important. It also includes building a test corpus so they can track their progress. - Design a specific problem statement with measurable success criteria. Impact is a judging criterion and a performance metric in this phase. - Create a metadata/knowledge graph approach with a clear implementation plan - Present your challenge design to the Bloomberg Office of the CTO, metadata, for validation, feedback, & refinement Phase 2: Solution Development (2-4 weeks, launch date TBD) - Let AskB engineering teams (SMEs) build a working prototype using Bloomberg's metadata/knowledge graph tools and AskB documentation - Mentor AskB team so they implement your metadata optimization approach - Guide them to measure performance improvements (speed, accuracy, user satisfaction) - Let them document the methodology and results Phase 3: Validation & Assessment (1 week, after phase 2) - Teams, including yourself as the team's mentor, present their solution to the Office of the CTO team for technical review - Demonstrate measurable improvements to AskB functionality - Complete a knowledge graph competency assessment - Receive certification in Bloomberg metadata management practices ## Expected Outcomes PHASE 1: Successful solutions will deliver: - Proposed AskB Challenge: A real AskB challenge that you believe a knowledge graph can address with a demonstrable solution - Business Case Impact & Performance Metrics: Quantified expected improvements in AI response time, accuracy, or user experience - Technical Documentation: Clear methodology showing how knowledge graphs should optimize your chosen AI process - Case for KG: Evidence-based recommendation for addressing the problem with a KG solution - Knowledge Graph Mastery: Competency in metadata management and graph database techniques that the AskB team will need to develop Details for PHASE 2 & 3 will be given later. This challenge offers a unique opportunity to work directly with Bloomberg's AI innovation, letting them realize the opportunity to ground and optimize AI agents with knowledge graphs, while letting them master the knowledge graph techniques that are becoming essential for modern data science careers - under your mentorship.