Journal:Research on information retrieval model based on ontology
| Full article title | Research on information retrieval model based on ontology |
|---|---|
| Journal | EURASIP Journal on Wireless Communications and Networking |
| Author(s) | Yu, Binbin |
| Author affiliation(s) | Jilin University, Beihua University |
| Primary contact | Email: yubinbin80 at sina dot com |
| Year published | 2019 |
| Volume and issue | 2019 |
| Page(s) | 30 |
| DOI | 10.1186/s13638-019-1354-z |
| ISSN | 1687-1499 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-019-1354-z |
| Download | https://jwcn-eurasipjournals.springeropen.com/track/pdf/10.1186/s13638-019-1354-z (PDF) |
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Abstract
An information retrieval system not only occupies an important position in the network information platform, but also plays an important role in information acquisition, query processing, and wireless sensor networks. It is a procedure to help researchers extract documents from data sets as document retrieval tools. The classic keyword-based information retrieval models neglect the semantic information which is not able to represent the user’s needs. Therefore, how to efficiently acquire personalized information that users need is of concern. The ontology-based systems lack an expert list to obtain accurate index term frequency. In this paper, a domain ontology model with document processing and document retrieval is proposed, and the feasibility and superiority of the domain ontology model are proved by the method of experiment.
Keywords: ontology, information retrieval, genetic algorithm, sensor networks
Introduction
Information retrieval is the process of extracting relevant documents from large data sets. Along with the increasing accumulation of data and the rising demand of high-quality retrieval results, traditional information retrieval techniques are unable to meet the task of high-quality search results. As a newly emerged knowledge organization system, ontology is vitally important in promoting the function of information retrieval in knowledge management.
Existing information retrieval models, such as the vector space model (VSM)[1], are based on certain rules to model text in pattern recognition and other fields. For example, a VSM splits, filters, and classifies text that looks very abstract and using certain rules calculates statistics such as word frequency.
Probability models[2] mainly rely on probabilistic operation and Bayes rules to match data information, in which the weight values of feature words are all multivalued. The probabilistic model uses the index word to represent the user’s interest, that is, the personalized query request submitted by the user. Meanwhile, there is no vocabulary set with a standard semantic feature and document label. Traditional weighted strategies lack semantic information of the document, which is not representative for the document description. On the basis of semantic annotation results, weighted item frequency[3] and domain ontology of the semantic relation are used to express the semantics of the document.[4]
References
- ↑ Tang, M.; Bian, Y.; Tao, F. (2010). "The Research of Document Retrieval System Based on the Semantic Vector Space Model". Journal of Intelligence 5 (29): 167–77. http://en.cnki.com.cn/Article_en/CJFDTOTAL-QBZZ201005036.htm.
- ↑ Ma, C.; Liang, W.; Zheng, M. et al. (2016). "A Connectivity-Aware Approximation Algorithm for Relay Node Placement in Wireless Sensor Networks". IEEE Sensors Journal 16 (2): 515-528. doi:10.1109/JSEN.2015.2456931.
- ↑ Yang, X.Q.; Yang, D.; Yuan, M. (2014). "Scientific Literature Retrieval Model Based on Weighted Term Frequency". Proceedings of the 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing: 427–430. doi:10.1109/IIH-MSP.2014.113.
- ↑ Xu, M.; Yang, Q.; Kwak, K.S. (2016). "Distributed Topology Control With Lifetime Extension Based on Non-Cooperative Game for Wireless Sensor Networks". IEEE Sensors Journal 16 (9): 3332-3342. doi:10.1109/JSEN.2016.2527056.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar and punctuation was edited to American English, and in some cases additional context was added to text when necessary. In some cases important information was missing from the references, and that information was added.
