represented largely depend on one or more business use cases. As
we designed our framework with industry application in mind, we need
to consider it within its real-world usage context.
Embedding-based similar term extraction
ConceptNet synonym extraction
WordNet synonym extraction
We choose Python as it eases access to the vast python
community and its library ecosystem, particularly NLP tools and
numerous Machine Learning (ML) libraries.
Algorithm implemented
Upcoming implementation
      : Iterative process
Our vision is to implement a toolbox of methods we can
gather to build pipelines. These pipelines can be run,
optimised and analysed to learn the best possible
ontology. 
Our implementation is largely based on the Python NLP
library spaCy. The text processing on spaCy helps us
work with data in many different languages while
staying flexible on the methods used. The only constraint
is to end up with a list of spaCy Doc objects.
OLAF IN A PRACTICAL CONTEXT
We designed the proposed framework focusing on automation with very little, if any, human involvement in mind. Unlike most existing approaches,
particular attention is brought to the learned ontology final production use case. We implement the framework as an open-source and open-
access python library. We aim to gather feedback and grow a community to develop and test multiple algorithms. Various satellite tools could be
developed to enhance the framework implementation. However, we should focus on developing axiom extraction and automatic ontology
evaluation. One exciting research area might be the adaptation of the software industry's "DevOps" concepts to knowledge management. The latter
field is known as "SemOps".
Different serialization techniques can be used to export and
leverage the learned ontology in an application system. 
ONTOLOGY LEARNING FRAMEWORK
ARCHITECTURE
a search engine on Schneider Electric products
a chatbot on Human Resources issues.
We only work on unstructured textual data. 
We apply the framework in two different use cases and datasets
to validate our results :
Our framework provides several algorithms for the different
stages of the pipeline. The algorithms are taken from external
libraries or directly implemented in the framework.  The goal is to
have as many methods as possible to cover the maximum needs.