Fuzzy conceptualization of the search queries

Deciding the context of the search query based on its representation over a concept network using fuzzy methods provides a better thrust to the overall search process. The existing context based search diversification methods emphasize the importance of the numerical representation of the query over...

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Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 16; H. 2; S. 957 - 965
Hauptverfasser: Sijin, P., Champa, H. N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.02.2024
Springer Nature B.V
Schlagworte:
Algorithms
Ambiguity
Artificial Intelligence
Computer Imaging
Computer Science
Diversification
Image Processing and Computer Vision
Keywords
Labeling
Machine Learning
Natural language processing
Original Research
Pattern Recognition and Graphics
Queries
Representations
Search process
Segmentation
Segments
Semantic analysis
Semantics
Software Engineering
Vision
Concept network
Singleton score
Maximal clique
Concept labeling
Conceptual ring
ISSN:2511-2104, 2511-2112
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Zusammenfassung:Deciding the context of the search query based on its representation over a concept network using fuzzy methods provides a better thrust to the overall search process. The existing context based search diversification methods emphasize the importance of the numerical representation of the query over a data repository and derive a query peace which is conceptually measured. The search operation can use these semantically meaningful segments as a confident segment in the conceptual network. The proposed Fuzzy Conceptual Search Model (FCM) derives a query string which is contextually measured by means of valid segmentation, Pairwise Type Detection and concept labeling on its Concept Network. The approach measures the semantic coherence among segments in the search query and prepares a valid query piece. The Maximal Clique Cycle Count (MCCC) algorithm is used in valid segmentation process. The novel randomized MCCC algorithm improved the quality of query segmentation process and considered shared segments and conceptual ring count to improvise the efficiency of the search. The segmented query paths are processed with proper instances to pinpoint the context of the search at an earlier phase. The instance ambiguity is removed by conducting a threshold pair wise modeling of typed terms followed by a weighted voting approach called Concept labeling. The proposed FCM shows good results in terms of precision for both query and tweet data sets with varying impact lexical feature indication values.
Bibliographie:ObjectType-Article-1
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01449-7