The Entity Metadata Ingestion and Distribution (EMID) team leads a company-wide effort to build scalable, interoperable linked data systems. Our mission is to aggregate and distribute metadata to support entity disambiguation across Bloomberg. Our data pipelines process over 10 million daily updates from streaming endpoints and cloud-hosted files, with enrichment and delivery latencies averaging just 700 ms per record. The resulting data underpins billions of data points utilised by applications throughout Bloomberg including but not limited to Trading Platforms and AI.
Having made excellent progress on our initial milestones, we're now expanding into the next phase: Transforming raw data into interconnected knowledge. We are building an inference platform for scalable management and execution of data-inferencing based on semantic models and user-defined rules to enrich raw datasets. Our ontology-based inferencing will also enable context-aware query and discovery, allowing users to explore implicit relationships and linked data patterns within Bloomberg's enterprise knowledge graph.
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