A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

Mingying Xu,Junping Du,Jiaxin Yang,Feifei Kou,Meiyu Liang,Xin Xu,Bo Rong

doi:10.1155/2021/4411524

Abstract

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

Highlights

Internet of Things technology has contributed to the realization of many Internet of Things applications that benefit the whole world and constantly changes people’s way of life
In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server
knowledge-enhanced representation learning module (KER)-SM: Academic search is implemented based on knowledge-enhanced feature representation; the academic text matching of which is based on interactive matching and semantic matching

Summary

Introduction

Internet of Things technology has contributed to the realization of many Internet of Things applications that benefit the whole world and constantly changes people’s way of life. Explicit Semantic Ranking [3] (ESR) defines academic retrieval as entity set retrieval, which represents the query and each document using knowledge graph embedding, and uses manually labeled training data to train a supervised academic search model. (1) An academic search framework based on Internet of Things technology is designed (2) A novel weakly supervised academic search model based on the knowledge-enhanced feature representation is proposed to improve academic search performance (3) In the process of academic search, the word-level interactive matching and the sentence-level semantic matching between query and document are considered to realize the accurate matching between query and document (4) Extensive experiments on the academic datasets prove that our proposed model is significantly better than the state-of-the-art search methods.

Related Work

Results

Experiments Settings

Experiments Setting and Training Details

Result

Conclusion