Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval

Abstract

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

The issues of design of a Petri net-based software component for modelling holistic and coordinated curriculum for potential specialists’ training

“…The frame-based model was opted for among other common AI models (semantic networks, productive rules, scripts etc.) due to its powerful built-in features in terms of educational content representation [12,13]. According to the frames theory which also takes essential roots from psychology, the frame-based representation reflects conceptional basis of the human memory organization, enables flexibility and visualization [13][14][15].…”

Hybrid optimized query expansion strategy for semantic information retrieval using spatial bound whale and binary moth flame optimization algorithm

“…The large‐scale neuromorphic architectures such as CerebelluMorphic 25 and spiking neural network 26 are yet to be explored in the area of information processing which when implemented offers huge benefits. In previous work, 27 we have proposed an improved Aquila Optimization based COOT (IAOCOOT) for query expansion and information retriveal.…”

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

“…When evaluated using the CACM, CISI, and MEDLINE datasets. Kumar et al 36 presented an Improved Aquila Optimization‐based COOT algorithm (IACOOT) for semantic document retrieval. The authors mainly enhanced the relevance matching score between the user query and document by identifying the semantic similarity that exists between them via a modified Needleman‐Wüst algorithm.…”

“…These authors 35–37,40–44 mainly applied the GOA algorithm due to its ability to preserve the feasibility of newly generated solutions. When compared to the PSO and BI algorithms, the GOA algorithm improves the capability of the FLC in handling high‐dimensional problems and overcoming local minima issues.…”

“…Fuzzy Logic Controller gains wide popularity due to its application in different complicated design mechanisms such as the Wireless Sensor Networks (WSN), 40 ground motion prediction, 41 Medical image fusion, 42 disease dataset feature selection, 43 big data sonar classification, 44 and vibration control of complex systems. 35 These authors [35][36][37][40][41][42][43][44] mainly applied the GOA algorithm due to its ability to preserve the feasibility of newly generated solutions. When compared to the PSO and BI algorithms, the GOA algorithm improves the capability of the FLC in handling high-dimensional problems and overcoming local minima issues.…”