Fuzzy Logic in Knowledge Management: A Model for Adaptive Information Access.
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| Title: | Fuzzy Logic in Knowledge Management: A Model for Adaptive Information Access. |
|---|---|
| Authors: | N., Yogeesh1 yogeesh.r@gmail.com, Chetana, R.2, T. N., Vasanthakumari1, M. S., Ramesha3 |
| Source: | Library of Progress-Library Science, Information Technology & Computer. Jul-Dec2024, Vol. 44 Issue 3, p14433-14441. 9p. |
| Subject Terms: | *Information storage & retrieval systems, Mathematical proofs, Fuzzy logic, Large scale systems, Fuzzy sets, Centroid |
| Abstract: | This paper provides a design of fuzzy logic based adaptive information retrieval system for Knowledge Management (KM). Typically, the traditional Boolean-based retrieval models are too restrictive because they use binary logic that make them unable to take into account even partial relevance between items we have and user queries. In order to overcome these drawbacks, the proposed model integrates fuzzy sets, fuzzy inference systems and rule-based aggregation techniques capable of dealing with uncertainties for a more personalized retrieval experience. It models user queries as fuzzy sets of relevance, evaluates rules using max-min aggregation, and obtains crisp relevance scores via centroid-based defuzzification. The research examines mathematical proofs, examples of practical applications and how fuzzy logic is implemented in an extensive case study such as a digital library. Experiments using evaluation metrics for performance like F-measure, precision and recall confirm our system can outperform baseline algorithms reusable by interacting with partial information matches allowing a more adaptive access to information's. The study also addresses computational complexity issues and provides some guidelines toward optimizing for large scale deployment of the system. Era of future research calls for the blending of fuzzy logic and machine learning techniques such as hybrid models, real-time adaptive systems etc. to best functionality KM applications are concerned. [ABSTRACT FROM AUTHOR] |
| Copyright of Library of Progress-Library Science, Information Technology & Computer is the property of A.K. Sharma, Editor & Publisher and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 180918523 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fuzzy Logic in Knowledge Management: A Model for Adaptive Information Access. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22N%2E%2C+Yogeesh%22">N., Yogeesh</searchLink><relatesTo>1</relatesTo><i> yogeesh.r@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Chetana%2C+R%2E%22">Chetana, R.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22T%2E+N%2E%2C+Vasanthakumari%22">T. N., Vasanthakumari</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22M%2E+S%2E%2C+Ramesha%22">M. S., Ramesha</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Library+of+Progress-Library+Science%2C+Information+Technology+%26+Computer%22">Library of Progress-Library Science, Information Technology & Computer</searchLink>. Jul-Dec2024, Vol. 44 Issue 3, p14433-14441. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+proofs%22">Mathematical proofs</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Large+scale+systems%22">Large scale systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Centroid%22">Centroid</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper provides a design of fuzzy logic based adaptive information retrieval system for Knowledge Management (KM). Typically, the traditional Boolean-based retrieval models are too restrictive because they use binary logic that make them unable to take into account even partial relevance between items we have and user queries. In order to overcome these drawbacks, the proposed model integrates fuzzy sets, fuzzy inference systems and rule-based aggregation techniques capable of dealing with uncertainties for a more personalized retrieval experience. It models user queries as fuzzy sets of relevance, evaluates rules using max-min aggregation, and obtains crisp relevance scores via centroid-based defuzzification. The research examines mathematical proofs, examples of practical applications and how fuzzy logic is implemented in an extensive case study such as a digital library. Experiments using evaluation metrics for performance like F-measure, precision and recall confirm our system can outperform baseline algorithms reusable by interacting with partial information matches allowing a more adaptive access to information's. The study also addresses computational complexity issues and provides some guidelines toward optimizing for large scale deployment of the system. Era of future research calls for the blending of fuzzy logic and machine learning techniques such as hybrid models, real-time adaptive systems etc. to best functionality KM applications are concerned. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Library of Progress-Library Science, Information Technology & Computer is the property of A.K. Sharma, Editor & Publisher and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 14433 Subjects: – SubjectFull: Information storage & retrieval systems Type: general – SubjectFull: Mathematical proofs Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Large scale systems Type: general – SubjectFull: Fuzzy sets Type: general – SubjectFull: Centroid Type: general Titles: – TitleFull: Fuzzy Logic in Knowledge Management: A Model for Adaptive Information Access. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: N., Yogeesh – PersonEntity: Name: NameFull: Chetana, R. – PersonEntity: Name: NameFull: T. N., Vasanthakumari – PersonEntity: Name: NameFull: M. S., Ramesha IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul-Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09701052 Numbering: – Type: volume Value: 44 – Type: issue Value: 3 Titles: – TitleFull: Library of Progress-Library Science, Information Technology & Computer Type: main |
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