Accessing large-scale structured datasets such as WDC [21], having millions of tables coming from hundreds of thousands of sources is very challenging [11, 13, 14, 30, 31]. Even if one topic (e.g. Job postings) is of interest, Jobs tables in different sources have hundreds of different schemas, which significantly complicates both finding and querying them.

Contextual embeddings, such as ELMo and BERT [21], [49], assign each word a representation based on its context [44]. This research builds on an observation that the context for structured data can be encoded very differently from the traditional sentence and text-based context. This means that embeddings for the structured data can be constructed on different principles, and if properly optimized, they can be used to improve performance ML and AI-based tasks related to the structured data. Here we present several new types of tabular embeddings taking into consideration structure of columns, rows, and presence of metadata. We demonstrated that properly optimized embeddings in combination with ML and DL models show significant improvement on such important tasks as tabular column and tuple recognition.

Anecdotal evidence suggests that Web-search engines, together with the Knowledge Graphs and Bases, such as YAGO [46], DBPedia [13], Freebase [16], Google Knowledge Graph [52] provide rapid access to most structured information on the Web. However, taking a closer look reveals a so called "knowledge gap" [18] that is largely in the dark. For example, a person searching for a relevant job opening has to spend at least 3 hours per week for several months [2] just searching job postings on numerous online job-search engines and the employer websites. The reason why this seemingly simple task cannot be completed by typing in a few keyword queries into a search-engine and getting all relevant results in seconds instead of hours is because access to structured data on the Web is still rudimentary. While searching for a job we have many parameters in mind, not just the job title, but also, usually location, salary range, remote work option, given a recent shift to hybrid work places, and many others. Ideally, we would like to write a SQL-style query, selecting all job postings satisfying our requirements, but it is currently impossible, because job postings (and all other) Web tables are structured in many different ways and scattered all over the Web. There is neither a Web-scale generalizable algorithm nor a system to locate and normalize all relevant tables in a category of interest from millions of sources.Here we describe and evaluate on a corpus having hundreds of millions of Web tables [39], a new scalable iterative training data generation algorithm, producing high quality training data required to train Deep- and Machine-learning models, capable of generalizing to Web scale. The models, trained on such en-riched training data efficiently deal with Web scale heterogeneity compared to poor generalization performance of models, trained without enrichment [20], [25], [38]. Such models are instrumental in bridging the knowledge gap for structured data on the Web.

Correctly identifying the semantic label of a table column (e.g. artist ) or the domain of a tuple (e.g. Song) is crucial for data science tasks, such as schema matching, data cleaning and discovery. Existing data preparation and integration systems are known to make mistakes that need to be corrected by humans, which is labor-intensive and expensive especially at scale [19], [20], [25], [9], [8]. The accuracy is also known to suffer on dirty data. In this paper we define and evaluate tabular embeddings to help increase the accuracy.Embeddings, is a well-known dimensionality reduction technique, usually applied to represent (one-dimensional) text documents/sentences as vectors for further analytics [28], [7], [23]. Using embeddings lowers dimensionality, as well as the models trained with embeddings often exhibit higher accuracy, compared to the same trained without such, because the embeddings vectors store the context information. Our 2-dimensional embeddings are more suitable for tables rather than text. We justify their efficiency on fundamental tasks on tables such as classifying columns and tuples.We perform an extensive experimental evaluation and compare the Neural Network trained with tabular embeddings against the same trained without such embeddings. We report significant accuracy gains in tuple classification when using the Neural Network trained with our tabular embeddings - up to 17.6% delta in F-measure for Songs compared with the same without our embeddings. For training and evaluating all our embeddings and models, we use a large-scale WebTables dataset having ≈15 million tables coming from ≈ 248K English Web sources [12].

Schema matching, historically, is a sub-area of Data Integration, responsible for matching relational or semi-structured schemas to facilitate further data integration process. In a standard schema matching scenario, with two schemas, a semi-supervised matching algorithm would generate pairwise table and attribute matches. Having a correct mapping between two schemas enables many data integration scenarios, such as schema integration, data translation, schema evolution, mediated/global schema, reverse-engineering, and others [18], [20], [23], [29], [30].For Web scale datasets with millions of tables from hundreds of thousands of sources, for example WEBTABLES [11], schema matching in its classical format becomes computationally infeasible due to its quadratic complexity in the number of schemas. We make a step forward, by noticing, that such brute-force matching is no longer feasible, and is also not needed at scale. Instead, a scalable solution would be to match only the semantically relevant tables, which are much less numerous.WebLens, a scalable data integration system, first, trains Deep Learning models to find and match semantically similar tables, then derives mediated schemas for these subsets to enable uniform access to all relevant data. In this paper, we focus on a high-level description of the entire process and give an example of query processing. For all experiments in the paper, we use a large-scale structured dataset having more than 15 million of relational Web tables in English coming from more than 248 thousand of Web sources.

Here we present machine learning-based approach to automatic classify live and dead bacteria that can be used for rapid search for optimal antibiotics in case of bacterial infections. The patients must be promptly administered a most efficient medication because all delays significantly increase morbidity and mortality. We engineered a new technology allowing us to efficiently and rapidly capture bacterial cells from different biological samples and proceed with a rapid antibiotic susceptibility testing thereby bypassing the need to culture the bacterium. We developed a new machine learning and microscopy-based approach for rapid assessment of bacterial viability following tests with antibiotics. Also, we created a labeled dataset with ~100 images of live and dead bacteria stained with DAPI (DNA; blue) and FM4-64 (membrane; red) either treated with an antibiotic or untreated. We analyzed wild type (WT) and ampicillin-resistant (ampR) E. coli, WT and ampR S. aureus, and B. subtilis. For antibiotic susceptibility testing we used ampicillin, chloramphenicol and erythromycin. We extracted information about red and blue channels from the images and tried two machine learning classifiers for rapid assessment of viability of the bacteria. The classifiers Random Forest and J48 Decision Tree demonstrated precision 90.7% and 96%, recall 94.4% and 100%, and F-measure 92.5% and 95.2%, correspondingly, on 10-fold cross-validation.

Data profiling is a "set of statistical data analysis activities and processes to determine properties of a given dataset". Historically,most of the data profiling tasks were aimed at data. At scale, when a dataset has millions of tables, their meta-data (i.e. titles, attribute names and types) becomes abundant similar to data instances, and its profiling starts playing a vital role.

Here we discuss our initial efforts towards unsupervised clustering of a large-scale Web tables dataset. We improve our previous approach of weakly-supervised clustering, where an operator would provide a few descriptive keywords to generate an entity-identifying classifier, which is applied to the corpora to form a cohesive entity-centric cluster [1]. Here, we make a next step towards fully unsupervised algorithm by automatically generating these descriptive keywords. These keywords then can be used to generate high-precision training data and train a classifier to form a cluster. Here, we describe and evaluate this new unsupervised keyword generation algorithm and apply it to a large-scale Web tables corpus to form initial small high-precision clusters.