Abstract
Knowledge graphs are proving to be an increasingly important part of modern enterprises, and new applications of such enterprise knowledge graphs are still being found. In this paper, we report on the experience with the use of an automatic knowledge graph system called Saffron in the context of a large financial enterprise and show how this has found applications within this enterprise as part of the “Conversation Concepts Artificial Intelligence” tool. In particular, we analyse the use cases for knowledge graphs within this enterprise, and this led us to a new extension to the knowledge graph system. We present the results of these adaptations, including the introduction of a semi-supervised taxonomy extraction system, which includes analysts in-the-loop. Further, we extend the kinds of relations extracted by the system and show how the use of the BERTand ELMomodels can produce high-quality results. Thus, we show how this tool can help realize a smart enterprise and how requirements in the financial industry can be realised by state-of-the-art natural language processing technologies.
| Original language | English |
|---|---|
| Article number | 160 |
| Number of pages | 160 |
| Journal | Information (Switzerland) |
| Volume | 12 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Apr 2021 |
Keywords
- FinTech
- Financial services
- Knowledge graphs
- Natural language processing
- Relation extraction
- Taxonomies
- Term extraction
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- McCrae, John P. and Mohanty, Pranab and Narayanan, Siddharth and Pereira, Bianca and Buitelaar, Paul and Karmakar, Saurav and Sarkar, Rajdeep
- John McCrae and Pranab Mohanty and Siddharth Narayanan and Bianca Pereira and Paul Buitelaar and Saurav Karmakar and Rajdeep Sarkar