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Intelligent Asset ManagementStorage and Update of Knowledge

Intelligent Asset Management: Storage and Update of Knowledge [Experience in developing large knowledge-based AI projects suggests a progressive approach: the system needs maintenance to keep pace with demands and accumulation of commonsense knowledge to prevent having to start all over again. Financial asset management is no exception. The balance between leveraging the current knowledge base and adding to it is analogous to the learning and thought relation described by Confucius. In the previous chapter, sentic computingSentic computing is actively “thinking” with the knowledge base, however, not learning anything. An example also shows the problem of unable to retrieve domain-specific concepts from the knowledge base. In this chapter, discussions on the forms of storing semantic and sentiment knowledge are presented. A special effort on adding and updating polarity scores of words with high-level supervision is made. The same idea can be extended to other application domains as well as the curation of concepts or events.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Intelligent Asset ManagementStorage and Update of Knowledge

Part of the Socio-Affective Computing Book Series (volume 9)
Xing, Frank; Cambria, Erik; Welsch, Roy
Intelligent Asset Management — Nov 14, 2019
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14 pages

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2019
ISBN
978-3-030-30262-7
Pages
97 –111
DOI
10.1007/978-3-030-30263-4_6
Publisher site
See Chapter on Publisher Site

Abstract

[Experience in developing large knowledge-based AI projects suggests a progressive approach: the system needs maintenance to keep pace with demands and accumulation of commonsense knowledge to prevent having to start all over again. Financial asset management is no exception. The balance between leveraging the current knowledge base and adding to it is analogous to the learning and thought relation described by Confucius. In the previous chapter, sentic computingSentic computing is actively “thinking” with the knowledge base, however, not learning anything. An example also shows the problem of unable to retrieve domain-specific concepts from the knowledge base. In this chapter, discussions on the forms of storing semantic and sentiment knowledge are presented. A special effort on adding and updating polarity scores of words with high-level supervision is made. The same idea can be extended to other application domains as well as the curation of concepts or events.]

Published: Nov 14, 2019

Keywords: Knowledge representation; Ontology engineering; Financial sentiment lexicon; Polarity score; Domain adaptation; Heuristic search

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