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Combining Semantic Web technologies and Computational Theory of Perceptions for text generation in financial analysis

Authors: Sheila Méndez Núñez; Gracián Triviño;

Combining Semantic Web technologies and Computational Theory of Perceptions for text generation in financial analysis

Abstract

The development of Internet technologies makes it possible to obtain data in near real time about the financial state of companies. Moreover, tools such as XBRL have been developed to deal with the automatic generation of business reports. However the available tools are not suitable to support the current tendency towards the so called, Continuous Reporting. Here, for a specific purpose, the wealth of available information should be summarized in short reports where the relevant information is remarked while the irrelevant remains hidden. Using a combination of Zadeh's Computational Theory of Perceptions and Semantic Web techniques, we propose a first approach to a computational framework to generate short reports that consist of few but meaningful Natural Language sentences. These sentences are designed to solve the user needs in a specific context of meaning defined with the help of an ontology. A practical demonstration of the computational framework is included.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Average
Top 10%
Top 10%
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