Technicians and maintenance managers in industrial environments would benefit from automatically extracting entities and relationships from different text data sources such as logs, event reports, and manuals. Extracting components from pieces of text and classifying them to the right failure type is not trivial in the domain specific setting where the vocabulary has specific meaning to the industry or domain, and labeled data set is very small. In this paper we address how to overcome these challenges in named entity recognition and classification of text, and present a way to improve the model iteratively and quickly. This interaction between components and related failures in the system can be represented in a knowledge graph, which enables further investigations such as Root Cause Analysis and Problem Diagnosis.

Enterprises often have a large number of databases and other sources of tabular data with columns full of domain-specific jargon (e.g. alpha-numeric codings, undeclared abbreviations, etc) which usually require domain experts to decode. Due to the jargon-specific content of the tables, no pre-trained language model such as Wiki2Vec [21] can be applied readily to encode the cell semantics due to absence of unique jorgan words or alpha-numeric codes in the model vocabulary. We propose a deep learning based framework that is ideally suited for serverless computing environment, and that 1) uses a new tokenization method, called Cell-Masking, 2) encodes the semantics of the cells into contextual embedding that exploits the locality features in tabular data, called Cell2Vec, and 3) an attention-based neural network, called TableNN, that provides a supervised learning solution to classify cell entries into predefined column classes. We apply the proposed method on three publicly available datasets of varying data sizes, from different industries. Cell-Masking provides an order of magnitude lower loss value and quickest convergence for cell embedding generation. In Cell2Vec, we demonstrate that the inclusion of row and column context improves the quality of embeddings by better loss curve convergence and improvement in accuracy by 5.4% on the BTS dataset [3].