FlexER：针对多个意图的灵活实体解析
FlexER: Flexible Entity Resolution for Multiple Intents
摘要：
实体解析是数据清洗和集成的一个长期问题，旨在识别代表同一真实世界实体的数据记录。现有方法将实体解析视为通用任务，假设存在对真实世界实体的单一解释，并且仅关注于找到匹配的记录，相对于该单一解释将对应的记录与非对应的记录分离。然而，在实体解析是更一般的数据项目的一部分的真实世界场景中，下游应用可能具有与例如各种用户需求相关的真实世界实体的不同解释。在下文中，我们将介绍多意图实体解析（MIER）的问题，这是对通用（单意图）实体解析任务的扩展。作为一个解决方案，我们提出了FlexER，利用当代的解决方案，通用的实体解析任务，以解决多意图实体解析。FlexER将该问题作为多标签分类问题来解决。它使用多路复用图表示来组合元组对的基于意图的表示，该多路复用图表示用作图神经网络（GNN）的输入。FlexER学习意图表示并改进多个解决方案问题的结果。一个大规模的实证评估引入了一个新的基准，也使用两个著名的基准，显示FlexER有效地解决了MIER问题，并优于国家的最先进的通用实体解决方案。
英文摘要：
Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, assuming the existence of a single interpretation of a real-world entity and focusing only on finding matched records, separating corresponding from non-corresponding ones, with respect to this single interpretation. However, in real-world scenarios, where entity resolution is part of a more general data project, downstream applications may have varying interpretations of real-world entities relating, for example, to various user needs. In what follows, we introduce the problem of multiple intents entity resolution (MIER), an extension to the universal (single intent) entity resolution task. As a solution, we propose FlexER, utilizing contemporary solutions to universal entity resolution tasks to solve multiple intents entity resolution. FlexER addresses the problem as a multi-label classification problem. It combines intent-based representations of tuple pairs using a multiplex graph representation that serves as an input to a graph neural network (GNN). FlexER learns intent representations and improves the outcome to multiple resolution problems. A large-scale empirical evaluation introduces a new benchmark and, using also two well-known benchmarks, shows that FlexER effectively solves the MIER problem and outperforms the state-of-the-art for a universal entity resolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
