  We build a common-knowledge concept recognition system for a Systems
Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such
as relation extraction, knowledge graph construction, and question-answering.
The problem is formulated as a token classification task similar to named
entity extraction. With the help of a domain expert and text processing
methods, we construct a dataset annotated at the word-level by carefully
defining a labelling scheme to train a sequence model to recognize systems
engineering concepts. We use a pre-trained language model and fine-tune it with
the labeled dataset of concepts. In addition, we also create some essential
datasets for information such as abbreviations and definitions from the systems
engineering domain. Finally, we construct a simple knowledge graph using these
extracted concepts along with some hyponym relations.
