Many tables on the web suffer from multi-level and multi-type quality problems, but existing cleaning systems cannot provide a comprehensive quality improvement for them. Most of these systems are designed for solving a specific type of error, so that we need to resort to a number of different cleaning tools (one per error type) to get a high quality table. In this demonstration, we propose a human-in-the-loop cleaning platform EasyDR for detecting and repairing multi-level&multi-type errors in tables. The attendees will experience the following features of EasyDR: 1) Holistic error detection&repair. Users are able to perform a holistic table cleaning in EasyDR where machine algorithms are responsible for error detection while human intelligence is leveraged for error repairing. 2) Human-in-the-loop table cleaning. EasyDR performs an all-round quality diagnosis for the table, and automatically generates crowdsourcing cleaning tasks for the detected errors. To simplify cleaning tasks for crowdsourcing workers, EasyDR provides two task optimization techniques including domain-aware table summarization and difficulty-aware task order optimization. 3) Customizable cleaning mode. EasyDR provides a declarative language for users to customize cleaning tasks flexibly, e.g., selecting target errors, restricting the cleaning scope, defining the cooperation mode for machine and crowd.
Identifying notable tuples in a web table is of great help for table understanding and table summarization. However, existing document-internal feature-based methods are inappropriate for identifying notable tuples in web tables. Additionally, for the web table describing multiple concepts, the notability evaluation of a tuple needs to take into account multiple entities as well as their importance in this tuple. In this paper, we investigate the task of identifying notable tuples in a multi-concept web table and propose a framework that includes three tasks: (1) identify multiple entity columns and their importance weights by building a column correlation graph based on types and relationships in the table; (2) obtain fine-grained entity notability scores based on entity link graph and provide solution for entity link failure and entity domain neglection; and (3) evaluate tuple notability by a weighted sum of notability scores of all entities in the tuple. Comprehensive evaluation of our approach is based on real-world web tables. The results demonstrate that our approach outperforms the state-of-the-art baselines by 4.6% on the precision of detecting multiple entity columns and by 12.5% on the metric normalized discounted cumulative gain (NDCG) of evaluating entity notability.
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