FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs

TL;DR

This paper introduces FinMatcher, a system that leverages multiple knowledge graphs and neural classifiers to accurately detect hypernyms in the financial services domain, addressing the FinSim-2 shared task.

Contribution

The paper presents a novel approach combining knowledge graphs and neural networks for hypernym detection in financial concepts, improving upon previous methods.

Findings

Achieved high accuracy in the FinSim-2 shared task

Effectively integrated WordNet, Wikidata, and WebIsALOD

Demonstrated the benefit of combining explicit and latent features

Abstract

This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference. The FinSim-2 shared task consists of a set of concept labels from the financial services domain. The goal is to find the most relevant top-level concept from a given set of concepts. The FinMatcher system exploits three publicly available knowledge graphs, namely WordNet, Wikidata, and WebIsALOD. The graphs are used to generate explicit features as well as latent features which are fed into a neural classifier to predict the closest hypernym.