Modified Firefly Algorithm and Fuzzy C-Mean Clustering Based Semantic Information Retrieval
As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.
Marie-Aude, Aufaure, On-Line semantic Infolrmation retrieval using Ontologies, IEEE, 2007.
Zheng, and Song-Nian, Ontology-Based Inverted Tables in Information Retrieval System, ICSKG, p. 354-357,2007.
Been-Chian Chien, Chih-Hung Hu Intelligent Information Retrieval Applying Automatic Constructed Fuzzy Ontology, ICMLC, pp. 2239- 2244,2007.
C. Carpineto, G. Romano. A Survey of Automatic Query Expansion in Information Retrieval‖. ACM Comput. Surv. 44, 1,2012..
K. Soner K., A. Özgür, An ontology-based retrieval system using semantic indexing‖. Inf. Syst. Vol.37, issue 4 ,pp 294-305,2004.
Castells, P., Fernandez, M. An adaptation of the vector-space model for ontology-based information retrieval, IEEE knowledge and data engineering, vol.19, iss. 2, 2007.
Varelas, G., Voutsakis, E., Semantic similarity methods in wordNet and their application to information retrieval on the web. ACM international workshop on Web information and data management (pp. 10-16), 2007.
Shyu, C. R., Klaric, GeoIRIS: Geospatial information retrieval and indexing system—Content mining, semantics modeling, and complex queries. IEEE Transactions on geoscience and remote sensing, 45(4), 839-852,2007.
Zhuhadar, Leyla, Olfa Nasraoui. "Semantic information retrieval for personalized e-learning." IEEE Conference on Tools with Artificial Intelligence, 2008.
Rinaldi, A. M. An ontology-driven approach for semantic information retrieval on the web. ACM Internet Technology (TOIT), vol.9, iss.3, 2009.
Kumar, M.S., N. Prakash Developing university an ontology in education domain using protégé for semantic web. Int J. Eng. Sci. Technol., 2: 4673-4681,2010.
. Lord, P., Stevens, R., Investigating Semantic Similarity Measures across the Gene Ontology: the Relationship between Sequence and Annotation. Bioinformatics, vol.19, iss,.10,pp: 1275–83,2003.
Pablo Castells, Miriam Fernández, Self-tuning Personalized Information Retrieval in an Ontology-Based Framework, International conference on “On the Move to Meaningful Internet Systems”, paper 11.3.4, pp. 977-986,2005.
Remi, S., & Varghese, S. C. Domain ontology driven fuzzy semantic information retrieval. Procedia Computer Science, 46, 676-681,2015.
Kara, Soner, et al. An ontology-based retrieval system using semantic indexing, Information Systems, vol.37, iss..4,pp: 294-305,2012.
Fouad, K. M., Khalifa, Web-based Semantic and Personalized Information Retrieval Semantic and Personalized Information Retrieval Semantic and Personalized Information Retrieval, 2012.
J. LUO, X. XUE. Research on Information Retrieval System Based on Semantic Web and Multi-Agent, International Conference on Intelligent Computing and Cognitive Informatics, .2012..
Hu, J., Lu, X., & Guan, C. A Semantic Information Retrieval Approach Based on Rough Ontology. The Open Cybernetics & Systemics Journal, vol.8,pp: 399-404,2014.
Zidi, A., & Abed, M. A generalized framework for ontology-based information retrieval: Application to a public-transportation system. In Advanced Logistics and Transport (ICALT), 2013.
Fernández, M., Cantador, I., López, V., Vallet, D., Castells, P., & Motta, E. Semantically enhanced information retrieval: An ontology-based approach. Web semantics: Science, services and agents on the world wide web, 9(4), 434-452,2011.