Weighted Hashing-Based Capture Text Similarity Estimation with the Cross-Media Semantic Level
Keywords:
Web Mining, Semantic Level, Weighted Hashing, Text, Similarity Value, Cross-MediaAbstract
Web Mining is an emerging trend for the drastic advancement of the different data mining techniques. The web mining process comprises the sequence of operations that are comprises of the different languages those need to be processed effectively. The estimation of the similarity between the ontologies words and the sequences are computed. This paper proposed a Weighted Hashing Similarity Estimation (WHSE). The proposed WHSE model comprises of the weighted values for the estimated semantics. The computed semantics are updated in the hashing table for the estimation of the features in the variables. The proposed WHSE computes the similarity score for the extracted sematic word features in the ontology and computes the key words. The proposed WHSE model performance is comparatively examined with the existing technique. The measured recall, precision and accuracy value expressed that proposed WHSE achieves the 0.98 accuracy value for the semantic ontology. The comparative analysis expressed that proposed WHSE achieves the ~3% - 7% improvement than the existing technique for the semantic level.
Downloads
References
Kulmanov, M., Smaili, F. Z., Gao, X., & Hoehndorf, R. (2021). Semantic similarity and machine learning with ontologies. Briefings in bioinformatics, 22(4), bbaa199.
Chandrasekaran, D., & Mago, V. (2021). Evolution of semantic similarity—a survey. ACM Computing Surveys (CSUR), 54(2), 1-37.
Ishiguro, S., & Saito, S. (2021). The detrimental effect of semantic similarity in short-term memory tasks: A meta-regression approach. Psychonomic Bulletin & Review, 28(2), 384-408.
Zad, S., Heidari, M., Hajibabaee, P., & Malekzadeh, M. (2021, October). A survey of deep learning methods on semantic similarity and sentence modeling. In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0466-0472). IEEE.
Hayes, T. R., & Henderson, J. M. (2021). Looking for semantic similarity: what a vector-space model of semantics can tell us about attention in real-world scenes. Psychological Science, 32(8), 1262-1270.
Lu, K., Li, R., Chen, X., Zhao, Z., & Zhang, H. (2021). Reinforcement learning-powered semantic communication via semantic similarity. arXiv preprint arXiv:2108.12121.
Mohammed, S. M., Jacksi, K., & Zeebaree, S. (2021). A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 552-562.
Zhang, P., Huang, X., Wang, Y., Jiang, C., He, S., & Wang, H. (2021). Semantic similarity computing model based on multi model fine-grained nonlinear fusion. IEEE Access, 9, 8433-8443.
Jin, L., & Gildea, D. (2022, May). Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 710-715).
Kocoń, J., & Maziarz, M. (2021). Mapping WordNet onto human brain connectome in emotion processing and semantic similarity recognition. Information Processing & Management, 58(3), 102530.
Li, S., Abel, M. H., & Negre, E. (2021, May). Ontology-based semantic similarity in generating context-aware collaborator recommendations. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 751-756). IEEE.
Yang, F., Liu, Y., Ding, X., Ma, F., & Cao, J. (2022). Asymmetric Cross-Modal Hashing with High-Level Semantic Similarity. Pattern Recognition, 108823.
Vetter, D., Tithi, J. J., Westerlund, M., Zicari, R. V., & Roig, G. (2022). Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI. arXiv preprint arXiv:2208.04608.
Netisopakul, P., Wohlgenannt, G., Pulich, A., & Hlaing, Z. Z. (2021). Improving the state-of-the-art in Thai semantic similarity using distributional semantics and ontological information. Plos one, 16(2), e0246751.
Solomon, S., Cohn, A., Rosenblum, H., Hershkovitz, C., & Yamshchikov, I. P. (2021). Rethinking Crowd Sourcing for Semantic Similarity. arXiv preprint arXiv:2109.11969.
Kakad, S., & Dhage, S. (2021, March). Ontology construction from cross domain customer reviews using expectation maximization and semantic similarity. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 19-23). IEEE.
Wang, L., Zhang, F., Du, Z., Chen, Y., Zhang, C., & Liu, R. (2021). A hybrid semantic similarity measurement for geospatial entities. Microprocessors and Microsystems, 80, 103526.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Lamhot Naibaho, Kuldeep Singh Kaswan, Pankaja R., Shrishailappa Patil, Santosh Mitkari, Vivek Nivruttirao Waghmare
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
