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首页> 外文会议>International workshop on enterprise applications and services in the finance industry >Applying Ontology-Informed Lattice Reduction Using the Discrimination Power Index to Financial Domain
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Applying Ontology-Informed Lattice Reduction Using the Discrimination Power Index to Financial Domain

机译：使用歧视电力指数对金融领域应用本体信息通知的晶格

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Contemporary financial institutions are relying on varied and voluminous data and so they need advanced technologies to provide their customers with the best possible services. Capturing the meaning, or semantics, of data and presenting these semantics in simplified yet relevant models are key challenges to achieving this. Formal Concept Analysis (FCA) automates the analysis of properties and instances of the data, generating a lattice which groups properties and instances into concepts. This lattice can be used as automatically generated semantic structure describing the domain, yet the complexity and size of the resultant lattice render this technique unusable in most practical cases involving financial data. To tackle this, our Ontology-informed Lattice Reduction approach can guide the reduction of the lattices generated from financial sampled data. We validate the adaptation of the approach to the financial domain through a real-world asset allocation case study, demonstrating that the approach achieves good overall performance and relevant results.
机译：当代金融机构依赖于各种各样的数据，因此他们需要先进的技术，为客户提供最好的服务。在简化且相关模型中捕获数据和呈现这些语义的含义或语义是实现这一目标的关键挑战。正式概念分析（FCA）自动分析数据的属性和实例，生成将属性和实例组成的晶格。该晶格可以用作描述域的自动生成的语义结构，但所得格子的复杂性和大小在涉及财务数据的大多数实际案件中呈现不可用的这种技术。为了解决这一问题，我们的本体上通知的格子还原方法可以指导减少从金融采样数据产生的格子。我们通过现实世界资产分配案例研究验证对金融领域的方法，表明该方法达到了良好的整体绩效和相关结果。

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