domox

Domain Model Extractor: automatically extract a (first iteration) domain model from requirements. By means of Natural Language Processing (NLP), it will try to identify candidates for:

• Classes
• Properties
• Actions and
• Associations

Domox allows the user to:

• Import documents
• Analyze requirements therein
• Extract relations
• Generate a Domain Model (UML)
• Flag relations as irrelevant (i.e. exclude them from the Domain Model)
• View the (plant)UML model and
• Export it (for subsequent processing by other tools, e.g. Apache Causeway)
• Allow to trace back from the Domain Model elements to requirements.

Design follows the outline in [1]:

NLP Processing usually is as follows:

Glossary

• Corpus - A list of documents
• NER - Named Entity Recognition
• POS - Part of Speech (Taggigng)
• Sofa - Subject of Analysis
• Boilerplate
• Tokenization
• Sentence Splitting
• Parsing
• Co-reference Resolution (synonyms)
• Lemmatization
• Stemming
• Annotation (refrence to text, begin/end)
• Type System - output
• AE - Analysis Engine

References

[1] C. Arora, M. Sabetzadeh, L. Briand, and F. Zimmer (2016). Extracting Domain Models from Natural-Language Requirements: Approach and Industrial Evaluation. https://people.svv.lu/sabetzadeh/pub/MODELS16.pdf

[2] M. Javed, and Y. Lin (2020). iMER: Iterative Process of Entity Relationship and Business Process Models Extraction from the Requirements.

[3] P. Coad, E. Lefebvre, and J. De Luca (1999). Java Modeling in Color with UML. Enterprise Components and Processes. ISBN 0-13-011510-X

[4] Generating code via XTEXT https://github.com/echebbi/standalone-xtext-example https://blogs.itemis.com/en/building-domain-specific-languages-with-xtext-and-xtend

Excerpt from [1]:

Syntactic parsing (SP) is the key enabling NLP technology. It consists of:

• Phrase structure parsing
  • noun phrases (NP)
  • verb phrases (VP) / verb (VB)
• dependency parsing (functional constituents, parse tree)
  • subject
  • object

There are four kinds of relations:

• (regular) Association
• Aggregation
• Generalization
• Attribute

A Domain Model has:

• 1..n Concepts
• 1..n Attributes
• 1..n Associations

Various

Tutorials

• Tutorialspoint: https://www.tutorialspoint.
• Lucene NLP: https://fabian-kostadinov.github.io/2018/09/08/introduction-to-lucene-opennlp-part1/
• Baeldung: https://www.baeldung.com/apache-open-nlp
• https://pub.towardsai.net/natural-language-processing-nlp-with-python-tutorial-for-beginners-1f54e610a1a0

Ideas

• Colorize ClassCandidates by means of their properties/actions [3]

• temporal relations -> activity diagrams (HeidelTime?)

• use MaryTTS (http://mary.dfki.de:59125/) to create audio of sentences, store them as Blob and have it play (cf. https://www.w3schools.com/html/html5_audio.asp)

• Have StanfordNLP run in a docker image, using GPU:

  • https://stackoverflow.com/questions/60718574/containerization-of-a-python-code-with-stanfordnlp-that-uses-gpu
  • https://github.com/NLPbox/stanford-corenlp-docker

Tools

• Apache OpenNLP https://opennlp.apache.org/com/opennlp/opennlp_command_line_interface.htm
• Apache UIMA
• Apache cTAKES
• BRAT https://brat.nlplab.org/index.html
• Heideltime https://github.com/HeidelTime/heideltime
• FreeLing http://nlp.lsi.upc.edu/freeling/node/1

Search Terms

• Onlotogy learning/extraction https://en.wikipedia.org/wiki/Ontology_learning

Running with Docker

Kroki/PlantUML

Kroki is used for generating diagrams (UML, TypedDependency) - this can either be the instance the kroki team provides or one you host yourself as Docker image.

StanfordCoreNLP

StanfordCoreNLP can be used either from a jar library or via a service hosted by a Docker image. https://hub.docker.com/r/alborzi/stanford-corenlp

docker pull alborzi/stanford-corenlp