nist-nestor 0.2rc1

Introduction
============


Purpose
-------
This application was designed to help manufacturers “tag” their maintenance work-order data
according to the methods described in
:cite:`sexton2017hybrid,sharp2017toward`. The goal of this
application is to give understanding to data sets that previously were
too unstructured or filled with jargon to analyze. The current build is
in very early alpha, so please be patient in using this application. If
you have any questions, please do not hesitate to contact us (see
:ref:`who-are-we`. )




Why?
~~~~
There is often a large amount of maintenance data *already* available
for use in Smart Manufacturing systems, but in a currently-unusable
form: service tickets and maintenance work orders (MWOs). **Nestor** is a
toolkit for using Natural Language Processing (NLP) with efficient
user-interaction to perform structured data extraction with minimal
annotation time-cost.

Features
~~~~~~~~
- Ranks concepts to be annotated by importance, to save you time
- Suggests term unification by similarity, for you to quickly review
- Basic concept relationships builder, to assist assembling problem code and taxonomy definitions
- Strucutred data output as tags, whether in readable (comma-sep) or computation-friendly (sparse-mat) form.


What’s Inside?
~~~~~~~~~~~~~~
NOTE: this repository is under construction, and tutorial .ipynb
documents are still being written. Documentation is currently
code-internal only, however, dedicated documentation hosted through
sphynx/readthedocs.io is underway.

Current:

- Tagging Tool: Human-in-the-loop Annotation Interface (pyqt)
- Unstructured data processing toolkit (sklearn-style)
- Vizualization tools for tagged MWOs-style data (under development)


Planned/underway:

- KPI creation and visualization suite
- Machine-assisted functional taxonomy generation
- Quantitative skill assement and training suggestion engine
- Graph Database creation assistance and query tool

Pre-requisites
--------------
This package was built as compatible with Anaconda python distribution.
See our `requirements file <./requirements.txt>`__ for a complete
list of major dependencies.

ADD LIST


.. _who-are-we:

Who are we?
-----------
This toolkit is a part of the Knowledge Extraction and Application for
Smart Manufacturing (KEA) project, within the Systems Integration
Division at NIST.


Points of Contact
~~~~~~~~~~~~~~~~~

- `Michael Brundage <https://www.nist.gov/people/michael-p-brundage>`__ Principal Investigator
- `Thurston Sexton <https://github.com/tbsexton>`__ Technical Lead


Contributors:
~~~~~~~~~~~~~

:Thurston Sexton: @tbsexton
:Sascha Moccozet: @saschaMoccozet
:Madhusudanan Navinchandran: @msngit
:Michael Brundage: @MichaelPBrundage
:Emily Hastings: @emhastings
:Lela Bones: @lelatbones


Why KEA?
~~~~~~~~
The KEA project seeks to better frame data collection and
transformation systems within smart manufacturing as *collaborations*
between human experts and the machines they partner with, to more
efficiently utilize the digital and human resources available to
manufacturers. Kea (*nestor notabilis*) on the other hand, are the world’s only alpine parrots, finding their home on the southern
Island of NZ. Known for their intelligence and ability to solve puzzles
through the use of tools, they will often work together to reach their
goals, which is especially important in their harsh, mountainous
habitat.