JSI at the FinSim-2 task: Ontology-Augmented Financial Concept Classification
Timen Stepi\v{s}nik Perdih, Senja Pollak, Bla\v{z} \v{Skrlj}

TL;DR
This paper demonstrates how ontologies can be used for financial concept classification by transforming ontologies into graphs, applying semantic generalization, and combining graph search with word vectorization and machine learning.
Contribution
It introduces a practical ontology-based approach for classifying financial concepts into relevant hypernyms without requiring training data.
Findings
Effective classification of financial concepts into hypernyms
Combines graph search with word vectorization and machine learning
Achieves promising results on the FinSim-2 task
Abstract
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…
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