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Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle- in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augment- ing existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and bench- marks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset con- sists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query- table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table- search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a re- sult, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions. 

Modern data lakes are 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, because tables implicitly define a massive network of interconnected values. We introduceDomainNet, which efficiently represents this network, and investigate to what extent it can be used to disambiguate values without requiring any supervision. DomainNetleverages network-centrality measures on a bipartite graph whose nodes represent data values and attributes to determine 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 achieves an F1-score of 0.38 versus 0.69 forDomainNet, which separates homographs well from data values that have a unique meaning. On a real data lake, our top-100 precision is 93%. Given a homograph, we also present a novel method for determining the number of meanings of the homograph and for assigning its data lake attributes to a meaning. We show the influence of homographs on two downstream tasks: entity-matching and domain discovery.