DomainNet: Homograph Detection for Data Lake Disambiguation
DomainNet: Homograph Detection for Data Lake Disambiguation
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DOI：
10.5441/002/edbt.2021.03
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Aristotelis Leventidis;Laura Di Rocco;Wolfgang Gatterbauer;Renée J. Miller;Mirek Riedewald
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文献类型：
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作者：
Aristotelis Leventidis;Laura Di Rocco;Wolfgang Gatterbauer;Renée J. Miller;Mirek Riedewald
现代数据湖在用于描述数据的词汇表中具有很大的异构性。我们研究了数据湖中的消歧问题：我们如何确定一个数据值在湖中出现不止一次是否具有不同的含义，因此是一个同形异义词？虽然单词和实体消歧在计算语言学，数据管理和数据科学中已经得到了很好的研究，但我们表明，数据湖为数据值的消歧提供了一个新的机会，因为它们代表了一个庞大的互联值网络。我们调查在何种程度上，这个网络可以用来消除歧义的价值观。DomainNet在一个二分图上使用网络中心性度量，该图的节点表示值和属性，以在没有监督的情况下确定一个值是否是一个同形异义词。一个彻底的实验评估表明，国家的最先进的技术领域发现不能重新利用我们的方法竞争。具体来说，使用域发现方法来识别同形词具有38%的准确率和召回率，而我们的方法在合成基准上的准确率和召回率为69%。通过将网络中心性度量应用于我们的图表示，DomainNet实现了同形词和具有独特含义的数据值之间的良好分离。在一个真实的数据湖上，我们的前200名精度为89%。
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%.