DomainNet: Homograph Detection for Data Lake Disambiguation

Abstract

Modern data lakes are deeply heterogeneous in the vocabulary that is used to describe data. We study a problem of disambiguation in data lakes: how can we determine if a data value occurring more than once in the lake has different meanings and is therefore a homograph? While word and entity disambiguation have been well studied in computational linguistics, data management and data science, we show that data lakes provide a new opportunity for disambiguation of data values since they represent a massive network of interconnected values. We investigate to what extent this network can be used to disambiguate values.DomainNet uses network-centrality measures on a bipartite graph whose nodes represent values and attributes to determine, without supervision, if a value is a homograph. A thorough experimental evaluation demonstrates that state-of-the-art techniques in domain discovery cannot be re-purposed to compete with our method. Specifically, using a domain discovery method to identify homographs has a precision and a recall of 38% versus 69% with our method on a synthetic benchmark. By applying a network-centrality measure to our graph representation, DomainNet achieves a good separation between homographs and data values with a unique meaning. On a real data lake our top-200 precision is 89%.

Source-Free Domain Adaptation for Real-World Image Dehazing

“…Most SFDA methods focus on image classification, which is the fundamental task in computer vision. With the popularity of large-scale datasets like VisDA [130] and Domain-Net [142], the demand of the adaptation capability also increases. Image classification can also extend to various application scenarios such as real-world image dehazing [143], cross-scene hyperspectral image classification [144], and blind image quality assessment [114].…”