# JSI at the FinSim-2 task: Ontology-Augmented Financial Concept Classification

arXiv: https://arxiv.org/abs/2106.09230 · HTML: https://mlsanity.com/paper/2106.09230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification](https://mlsanity.com/paper/1902.00438/) (2019)

## Built on by (most cited first, 2 of 2)

- [Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms](https://mlsanity.com/paper/2107.13764/) (2021)
- [Learning Semantic Text Similarity to rank Hypernyms of Financial Terms](https://mlsanity.com/paper/2303.13475/) (2023)

## Abstract (arXiv)

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.