An empirical study of the rise of big data in business scholarship

Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Machine Learning for Financial Risk Management: A Survey

The number of applications being developed that require access to knowledge about the real world has increased rapidly over the past two decades. Domain ontologies, which formalize the terms being used in a discipline, have become essential for research in areas such as Machine Learning, the Internet of Things, Robotics, and Natural Language Processing, because they enable separate systems to exchange information. The quality of these domain ontologies, however, must be ensured for meaningful communication. Assessing the quality of domain ontologies for their suitability to potential applications remains difficult, even though a variety of frameworks and metrics have been developed for doing so. This article reviews domain ontology assessment efforts to highlight the work that has been carried out and to clarify the important issues that remain. These assessment efforts are classified into five distinct evaluation approaches and the state of the art of each described. Challenges associated with domain ontology assessment are outlined and recommendations are made for future research and applications.

Evaluating Domain Ontologies: Clarification, Classification, and Challenges

Heterogeneous data, different definitions and incompatible models are a huge problem in many domains, with no exception for the field of energy systems analysis. Hence, it is hard to re-use results, compare model results or couple models at all. Ontologies provide a precisely defined vocabulary to build a common and shared conceptualisation of the energy domain. Here, we present the Open Energy Ontology (OEO) developed for the domain of energy systems analysis. Using the OEO provides several benefits for the community. First, it enables consistent annotation of large amounts of data from various research projects. One example is the Open Energy Platform (OEP). Adding such annotations makes data semantically searchable, exchangeable, re-usable and interoperable. Second, computational model coupling becomes much easier. The advantages of using an ontology such as the OEO are demonstrated with three use cases: data representation, data annotation and interface homogenisation. We also describe how the ontology can be used for linked open data (LOD).

Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

Ontologies provide an opportunity to tackle the interoperability challenge in digital manufacturing. Although ontologies have been used in numerous industrial projects, the problem of semantic inte...

Industrial ontologies for interoperability in agile and resilient manufacturing

This article describes the Financial Industry Business Ontology (FIBO) as a set of formal models that define unambiguous shared meaning for financial industry concepts. An account is given of the history and development of the FIBO series of standards and the theoretical underpinnings of these as a business or ‘conceptual’ model. Some initial proof of concept work is described, demonstrating how in addition to the use of FIBO as a conceptual model, it is possible to derive semantic technology-based applications that may be used to carry out novel types of processing on data. The development roadmap of the FIBO series of standards within the Object Management Group is also described, so that readers can have an idea of what to expect from FIBO and when.

https://link.springer.com/content/pdf/10.1057%2Fjbr.2013.13.pdf

The financial industry business ontology: Best practice for big data

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The financial industry business ontology: Best practice for big data