Enriching Context Information for Entity Linking with Web Data

Wang, Yi Ting; Shen, Juntai; Li, Zhi Xu; Yang, Qiang; Liu, An; Zhao, Peng; Xu, Jia Jie; Zhao, Lei; Yang, Xun

doi:10.1007/s11390-020-0280-1

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Utilizes web settings to extend worldwide information, for example distinguish and utilize the most significant terms from web settings utilizing graphical displaying. At last, consolidate the two models to utilize expanded nearby and worldwide data from extra web settings [25]. WebEL effectively improves the performance of modern EL methods.…”

Section: Related Workmentioning

confidence: 99%

Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics

Chandrasekar

Venkatachalam

2023

Preprint

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Currently, online learning systems in the education sector are widely used and have become a new trend, generating large amounts of educational data based on students' activities. In order to improve online learning experiences, sophisticated data analysis techniques are required. Adding value to E-learning platforms through the efficient processing of big learning data is possible with Big Data. With time, the E-learning management system's repository expands and becomes a rich source of learning materials. Subject matter experts may benefit from using E-learning resources to reuse previously created content when creating online content. In addition, it might be beneficial to the students by giving them access to the pertinent documents for achieving their learning objectives effectively. An improved intelligent information retrieval and reliable storage (OIIRS) scheme is proposed for E-learning using hybrid deep learning techniques. Assume that relevant E-learning documents are stored in cloud and dynamically updated according to users' status. First, we present a highly robust and lightweight crypto i.e. optimized CLEFIA, for securely storing data in local repositories that improve the reliability of data loading. We develop an improved butterfly optimization (IBO) algorithm to provide an optimal solution for CLEFIA that selects private keys. In addition, a hybrid deep learning method, i.e., backward diagonal search based deep recurrent neural network (BD-DRNN) is introduced for optimal intelligent information retrieval based on keywords rather than semantics. Here, feature extraction and key feature matching are performed by the modified Hungarian optimization (MHO) algorithm that improves searching accuracy. Finally, we test our proposed OIIRS scheme with different benchmark datasets and use simulation results to test the performance.

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Section: Related Workmentioning

confidence: 99%