Human-in-the-loop data integration

Abstract

Data integration aims to integrate data in different sources and provide users with a unified view. However, data integration cannot be completely addressed by purely automated methods. We propose a hybrid human-machine data integration framework that harnesses human ability to address this problem, and apply it initially to the problem of entity matching. The framework first uses rule-based algorithms to identify possible matching pairs and then utilizes the crowd to refine these candidate pairs in order to compute actual matching pairs. In the first step, we propose similarity-based rules and knowledge-based rules to obtain some candidate matching pairs, and develop effective algorithms to learn these rules based on some given positive and negative examples. We build a distributed in-memory system DIMA to efficiently apply these rules. In the second step, we propose a selection-inference-refine framework that uses the crowd to verify the candidate pairs. We first select some "beneficial" tasks to ask the crowd and then use transitivity and partial order to infer the answers of unasked tasks based on the crowdsourcing results of the asked tasks. Next we refine the inferred answers with high uncertainty due to the disagreement from the crowd. We develop a crowd-powered database system CDB and deploy it on real crowdsourcing platforms. CDB allows users to utilize a SQL-like language for processing crowd-based queries. Lastly, we provide emerging challenges in human-in-the-loop data integration.