Tabular metadata (i.e., attributes in a table) identification and classification is a fundamental problem in large-scale data management of structured corpora, especially for complex tables rich in multi-level hierarchical metadata with nesting. Medical, security, data science research literature, Web tables, contain thousands of such complex tables, but often lack or incorrectly label their complex metadata. In this work, we describe an unsupervised, scalable, contrastive-learning approach for classification of multi-layer, hierarchical metadata in such tables. We compared it to the state of the art (SOTA) as well as the latest Large Language Models (LLMs), such as OpenAI GPT 3.5 and 4 with and without Retrieval Augmented Generation (RAG) on several large-scale heterogeneous datasets. We outperform SOTA and LLMs in classifying horizontal metadata (HMD) of deep levels (3–5) and for all levels (1–3) of vertical metadata (VMD). For HMD levels 1–2, SOTA outperforms us insignificantly, with a delta of ≈1%. LLMs with/without RAG slightly outperform us with deltas of 4–5% in accuracy for HMD level 1, but we significantly outperformed LLMs/LLMs+RAG with delta up to 29% for all other levels 2–5 HMD and up to 87% delta for VMD.

Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center and Research Institute, which is one of the top Cancer centers in the U.S. and in the world. Our hybrid is remarkable as it serves the user needs better than just an LLM, KG or a search-engine in isolation. LLMs as is are known to exhibit hallucinations and catastrophic forgetting as well as are trained on outdated corpora. The state of the art KGs, such as PrimeKG, cBioPortal, ChEMBL, NCBI, and other require manual curation, hence are quickly getting stale. CancerKG is unsupervised and is capable of automatically ingesting and organizing the latest medical findings. To alleviate the LLMs shortcomings, the verified KG serves as a Retrieval Augmented Generation (RAG) guardrail. CancerKG exhibits 5 different advanced user interfaces, each tailored to serve different data modalities better and more convenient for the user. We evaluated CancerKG on real user queries and report a high NDCG score on a large-scale corpora of approximately 44K publications.

Data profiling is a "set of statistical data analysis activities to determine properties of a dataset". Historically, it was aimed at data (not meta-data), but at scale, the tables’ meta-data (i.e. title, attribute names, types) becomes abundant, hence its profiling becomes vital, especially in order to understand the contents of large-scale structured datasets.Here we describe and evaluate the algorithms and models behind our scalable Meta-data profiler. It is capable of learning Meta-profiles for a topic of interest in extreme-scale structured datasets, such as WDC [1] or CORD-19 [2] having millions of tables and hundreds of thousands of sources. A 3D Meta-profile visualizes a specific topic (e.g. COVID-19 vaccine side-effects) present in a large-scale structured dataset and simplifies access and comparison for data scientists and end-users.

Accessing large-scale structured datasets such as WDC [31] or CORD-191 is very challenging [ 11, 13, 14, 41, 42]. Even if one topic (e.g. Vaccine Side-Effects) is of interest, the side-effects tables in different papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate our scalable Meta-data profiler, capable of constructing a standardized interface to a topic of interest in large-scale structured datasets. This interface, called Meta-profile represents a meta-data summary per each topic, representative of the entire dataset. Such profiles can be used as a robust visualization as well as to simplify access to structured data for both data scientists and end users at scale [32, 42]

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.

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.

Keyword-search engines (e.g. Web-search) usually can be outperformed by a specialized system optimized for a spe- cic domain, type of data, or queries (8, 2, 12, 5, 11, 9). For example, Halevy et. al. in (13) demonstrate how a special- ized Google Fusion Tables spatial search can outperform the general-purpose Google Web-search on bike trails search in San Francisco Bay Area. At the same time, Web content providers usually exhibit a specic focus for their postings. For example, information at http://www.csail.mit.edu is devoted to Computer Science research and education, Han- nah Montana is mostly tweeting about music, and the same is true for most sources. This paper describes the work in progress on a new Type- aware Web-search system that uses topical focus of informa- tion sources to process a large class of queries better than a regular Web search-engine. It leverages semantic proles similar to (10, 6, 7) and a new Type-aware Locality-Sensiti Hashing (TLSH) scheme to accomplish it.

Relevance of search-results is a key factor for any search engine. In order to return and rank the Web-pages that are most relevant to the query, contemporary search engines use complex ranking functions that depend on hundreds of features. For example, presence or absence of the query keywords on the page, their proximity, frequencies, HTML markup are just a few to name. Additional features might include fonts, tags, hyperlinks, metadata, and parts of the Web-page description. All this information is used by the search-engine to rank HTML Web pages returned to the user, but is unfortunately absent in free text that has no HTML markup, tags, hyperlinks, and any other metadata, except implicit natural language structure.