Application of the Financial Industry Business Ontology (FIBO) for development of a financial organization ontology

Petrová, Gabriela; Tuzovsky, A.F.; Aksenova, N. V.

doi:10.1088/1742-6596/803/1/012116

Cited by 12 publications

(16 citation statements)

References 12 publications

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“…In the financial world, the most well-known ontology at the moment is FIBO, which is developed by the Enterprise Data Management (EDM) Council [50]. A number of financial systems already support it, which allows businesses to exchange data corresponding to FIBO, in the semantic format [48].…”

Section: A Developing a Financial Ontologymentioning

confidence: 99%

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Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Yerashenia

Bolotov

2019

2019 IEEE 21st Conference on Business Informatics (CBI)

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In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company's financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database system.The Ontological layer defines the basic concepts of the financial risk management as well as the objects that serve as sources of knowledge for predicting a company's bankruptcy. The Graph Database layer utilises a powerful semantic data technology, which serves as a semantic data repository for our model.The article provides a detailed description of the construction of the Ontology and its informal conceptual representation. We also present a working prototype of the Graph Database system, constructed using the Neo4j application, and show the connection between well-known financial ratios.We argue that this methodology which utilises state of the art semantic data management mechanisms enables data processing and relevant computations in a more efficient way than approaches using the traditional relational database. These give us solid grounds to build a system that is capable of tackling the data of any complexity level.

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Section: A Developing a Financial Ontologymentioning

confidence: 99%

“…In fact, FIBO is not a single ontology, but a collection of a large number of ontologies, divided into modules and submodules [48]. The separate large modules include Financial Business and Commerce (FBC), Business Entities (BE), Securities (SEC), Indices and Indicators (IND), etc.…”

Section: A Developing a Financial Ontologymentioning

confidence: 99%

Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Yerashenia

Bolotov

2019

2019 IEEE 21st Conference on Business Informatics (CBI)

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“…Using ontology-based approach becomes useful technic in World-Wide Web for categorization [13,14,26]. The W3 consortium developing a dedicated framework for knowledge base builds, the Resource Description Framework.…”

Section: Ontologiesmentioning

confidence: 99%

“…What is FIBO? The role of financial industry business ontology is to centralize vocabularies of the financial industry [14]. In most of the implementations which rest on the ontology, there are three types of different ontological approaches which can operate separately or together and are used for the explicit description of semantics of the information resulting from heterogeneous databases.…”

Section: • Finally Using Instances Of Classmentioning

confidence: 99%

“…Using ontologies is the best approach to produce a universal model for metadata mapping between heterogeneous relational schemas [13]. We are focused on financial data integration, from which we choose financial industry business ontology (FIBO) for its rich content which is dedicated to financial contexts [14]. FIBO is used as global ontology and for each company, there is a local ontology which is defined for its information system metadata especially for financial modules.…”

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confidence: 99%

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An adaptive and real-time based architecture for financial data integration

et al. 2019

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The main goal of building an ETL is production data integration. Information systems have a various and large datasets of data following a relational schema in the most cases [1]. Companies use several tools for data preparation before loading into data warehouse, Microsoft SSIS, IBM Cognos, SAP Business Objects, Pentaho Data Integration and other tools are available for this type of tasks. Data warehouse provides a structured and cleansed data for reporting in order to provide metrics which allow firms to make the best decisions in business driving [2]. Financial reporting is based on financial metrics that allow internal and external regulatory and auditing bodies to evaluate the company's health. These metrics are extracted from financial data warehouse which contains datamarts of financial statements like statement of financial position. Companies are in obligation to financial statements delivery to government regulatory bodies [3], for example the general direction of taxes in Morocco requests mandatory statements delivery for every quarter then every year. Internal auditing needs also these financial statements to have an eagle eye on their business status. Financial data are seen as a large amount of various data, so there is several challenges in financial data processing. The

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Practices of Natural Language Processing in the Finance Sector

Bozyiğit

Kılınç

2022

Accounting, Finance, Sustainability, Governance &Amp; Fraud: Theory and Application

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Application of the Financial Industry Business Ontology (FIBO) for development of a financial organization ontology

Cited by 12 publications

References 12 publications

Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

An adaptive and real-time based architecture for financial data integration

Practices of Natural Language Processing in the Finance Sector

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