Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration
Topics: AI (Deep Learning), Data Mining, E-E-A-T, Ranking, Xin Luna Dong
The research paper explores the challenges of discovering high-quality data sources for integration in various applications. With the rapid growth of data sources, users struggle to find relevant and valuable sources while balancing quality and cost. The authors propose a data source management system that automatically evaluates data source quality and enables interactive exploration of sources based on predefined quality metrics such as coverage, accuracy, freshness, and cost. The system incorporates a correspondence graph linked to a knowledge base to facilitate better source selection and integration. The paper presents an architecture for the system and discusses key challenges in content analysis, source quality assessment, dependency modeling, and interactive exploration.