Solving Real-world Entity Resolution at Scale with AI
Tamr’s sophisticated AI-driven architecture employs multiple specialized AI techniques to tackle the complex challenge of entity resolution at scale.
Solve the Complex Challenge of Entity Resolution at Scale
Entity resolution is the process that identifies and links records that refer to the same real-world entity across multiple systems and datasets. It’s hard work, complicated by challenges such as the overwhelming volume of potential comparisons, imbalanced classification, and the absence of ground truth.
To solve entity resolution, Tamr employs a highly-sophisticated, scalable, and accurate AI-driven solution that integrates multiple, specialized techniques to enhance efficiency, accuracy, and adaptability. In this whitepaper, we’ll delve deep into core components of Tamr’s system, including:
- Feature Extraction, Blocking, and Pre-grouping
- Enrichment, Pairwise Classification, and Clustering
- Adaptive Learning, Categorization, and Semantic Search
We also explore Tamr’s proven ability to unify large and diverse datasets and future development opportunities shaped by continued advances in AI.
Download the white paper now.
Solve the Complex Challenge of Entity Resolution at Scale
Entity resolution is the process that identifies and links records that refer to the same real-world entity across multiple systems and datasets. It’s hard work, complicated by challenges such as the overwhelming volume of potential comparisons, imbalanced classification, and the absence of ground truth.
To solve entity resolution, Tamr employs a highly-sophisticated, scalable, and accurate AI-driven solution that integrates multiple, specialized techniques to enhance efficiency, accuracy, and adaptability. In this whitepaper, we’ll delve deep into core components of Tamr’s system, including:
- Feature Extraction, Blocking, and Pre-grouping
- Enrichment, Pairwise Classification, and Clustering
- Adaptive Learning, Categorization, and Semantic Search
We also explore Tamr’s proven ability to unify large and diverse datasets and future development opportunities shaped by continued advances in AI.
Download the white paper now.
