Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services
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 l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/4/160 |
| _version_ | 1797538204480438272 |
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| author | John P. McCrae Pranab Mohanty Siddharth Narayanan Bianca Pereira Paul Buitelaar Saurav Karmakar Rajdeep Sarkar |
| author_facet | John P. McCrae Pranab Mohanty Siddharth Narayanan Bianca Pereira Paul Buitelaar Saurav Karmakar Rajdeep Sarkar |
| author_sort | John P. McCrae |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:27:13Z |
| format | Article |
| id | doaj.art-9b64c686ef4b4ccb8c732e4cb8218bdb |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T12:27:13Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-9b64c686ef4b4ccb8c732e4cb8218bdb2023-11-21T14:56:24ZengMDPI AGInformation2078-24892021-04-0112416010.3390/info12040160Conversation Concepts: Understanding Topics and Building Taxonomies for Financial ServicesJohn P. McCrae0Pranab Mohanty1Siddharth Narayanan2Bianca Pereira3Paul Buitelaar4Saurav Karmakar5Rajdeep Sarkar6Insight SFI Research Centre for Data Analytics, Data Science Institute, NUI Galway, H91 A06C Galway, IrelandFMR LLC, Boston, MA 02110, USAFMR LLC, Boston, MA 02110, USAInsight SFI Research Centre for Data Analytics, Data Science Institute, NUI Galway, H91 A06C Galway, IrelandInsight SFI Research Centre for Data Analytics, Data Science Institute, NUI Galway, H91 A06C Galway, IrelandInsight SFI Research Centre for Data Analytics, Data Science Institute, NUI Galway, H91 A06C Galway, IrelandInsight SFI Research Centre for Data Analytics, Data Science Institute, NUI Galway, H91 A06C Galway, IrelandKnowledge 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.https://www.mdpi.com/2078-2489/12/4/160knowledge graphsFinTechtaxonomiesfinancial servicesnatural language processingrelation extraction |
| spellingShingle | John P. McCrae Pranab Mohanty Siddharth Narayanan Bianca Pereira Paul Buitelaar Saurav Karmakar Rajdeep Sarkar Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services Information knowledge graphs FinTech taxonomies financial services natural language processing relation extraction |
| title | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services |
| title_full | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services |
| title_fullStr | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services |
| title_full_unstemmed | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services |
| title_short | Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services |
| title_sort | conversation concepts understanding topics and building taxonomies for financial services |
| topic | knowledge graphs FinTech taxonomies financial services natural language processing relation extraction |
| url | https://www.mdpi.com/2078-2489/12/4/160 |
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