JSI at the FinSim-2 task: Ontology-Augmented Financial Concept
  Classification
  Ontologies are increasingly used for machine reasoning over the last few
years. They can provide explanations of concepts or be used for concept
classification if there exists a mapping from the desired labels to the
relevant ontology. Another advantage of using ontologies is that they do not
need a learning process, meaning that we do not need the train data or time
before using them. This paper presents a practical use of an ontology for a
classification problem from the financial domain. It first transforms a given
ontology to a graph and proceeds with generalization with the aim to find
common semantic descriptions of the input sets of financial concepts.
  We present a solution to the shared task on Learning Semantic Similarities
for the Financial Domain (FinSim-2 task). The task is to design a system that
can automatically classify concepts from the Financial domain into the most
relevant hypernym concept in an external ontology - the Financial Industry
Business Ontology. We propose a method that maps given concepts to the
mentioned ontology and performs a graph search for the most relevant hypernyms.
We also employ a word vectorization method and a machine learning classifier to
supplement the method with a ranked list of labels for each concept.
