2021/01/01 by Aristotelis Leventidis, Leventidis, Aristotelis, Laura Rocco +8 · 5 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Benchmark (surveying) #Bipartite graph #Centrality #Computer science #Data Quality and Management #Data mining #Databases (cs.DB) #FOS: Computer and information sciences #Graph #Linguistics #Mathematics #Natural language processing #Representation (politics) #Semantic Web and Ontologies #Statistics #Theoretical computer science #Vocabulary #cs.DB
paper · pdf · doi:10.48550/arxiv.2103.09940
published in arXiv (Cornell University), 13-24 (Cornell University) · Full version of paper appearing in EDBT 2021
openalex publication_date 2021/01/01 · arxiv created 2021/03/23 · arxiv updated 2021/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.