M. Loster, Tim Repke, Ralf Krestel, Felix Naumann, Jan Ehmueller, Benjamin Feldmann, Oliver Maspfuhl
{"title":"The Challenges of Creating, Maintaining and Exploring Graphs of Financial Entities","authors":"M. Loster, Tim Repke, Ralf Krestel, Felix Naumann, Jan Ehmueller, Benjamin Feldmann, Oliver Maspfuhl","doi":"10.1145/3220547.3220553","DOIUrl":null,"url":null,"abstract":"1 OVERVIEW & MOTIVATION The integration of a wide range of structured and unstructured information sources into a uniformly integrated knowledge base is an important task in the nancial sector. As an example, modern risk analysis methods can bene t greatly from an integrated knowledge base, building in particular a dedicated, domain-speci c knowledge graph. Knowledge graphs can be used to gain a holistic view of the current economic situation so that systemic risks can be identi ed early enough to react appropriately. The use of this graphical structure thus allows the investigation of many nancial scenarios, such as the impact of corporate bankruptcy on other market participants within the network. In this particular scenario, the links between the individual market participants can be used to determine which companies are a ected by a bankruptcy and to what extent. We took these considerations as a motivation to start the development of a system capable of constructing and maintaining a knowledge graph of nancial entities and their relationships. The envisioned system generates this particular graph by extracting and combining information from both structured data sources such as Wikidata and DBpedia, as well as from unstructured data sources such as newspaper articles and nancial lings. In addition, the system should incorporate proprietary data sources, such as nancial transactions (structured) and credit reports (unstructured). The ultimate goal is to create a system that recognizes nancial entities in structured and unstructured sources, links them with the information of a knowledge base, and then extracts the relations expressed in the text between the identi ed entities. The constructed knowledge base can be used to construct the desired knowledge graph. Our system design consists of several components, each of which addresses a speci c subproblem. To this end, Figure 1 gives a general overview of our system and its subcomponents.","PeriodicalId":161670,"journal":{"name":"Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3220547.3220553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
1 OVERVIEW & MOTIVATION The integration of a wide range of structured and unstructured information sources into a uniformly integrated knowledge base is an important task in the nancial sector. As an example, modern risk analysis methods can bene t greatly from an integrated knowledge base, building in particular a dedicated, domain-speci c knowledge graph. Knowledge graphs can be used to gain a holistic view of the current economic situation so that systemic risks can be identi ed early enough to react appropriately. The use of this graphical structure thus allows the investigation of many nancial scenarios, such as the impact of corporate bankruptcy on other market participants within the network. In this particular scenario, the links between the individual market participants can be used to determine which companies are a ected by a bankruptcy and to what extent. We took these considerations as a motivation to start the development of a system capable of constructing and maintaining a knowledge graph of nancial entities and their relationships. The envisioned system generates this particular graph by extracting and combining information from both structured data sources such as Wikidata and DBpedia, as well as from unstructured data sources such as newspaper articles and nancial lings. In addition, the system should incorporate proprietary data sources, such as nancial transactions (structured) and credit reports (unstructured). The ultimate goal is to create a system that recognizes nancial entities in structured and unstructured sources, links them with the information of a knowledge base, and then extracts the relations expressed in the text between the identi ed entities. The constructed knowledge base can be used to construct the desired knowledge graph. Our system design consists of several components, each of which addresses a speci c subproblem. To this end, Figure 1 gives a general overview of our system and its subcomponents.