FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 2 Mar 2021.
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6 of 24 references.
References · 6
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2020RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddingscited by 16
- 2020KGvec2go -- Knowledge Graph Embeddings as a Servicecited by 6
- 2020Supervised Ontology and Instance Matching with MELTcited by 5
- 2020Visual Analysis of Ontology Matching Results with the MELT Dashboardcited by 3
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.