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time-aware PolarisX: Auto-Growing Knowledge Graph

Computers, Materials & Continua 2021年第6期67卷 2695-2708页

作者： Yeon-Sun Ahn Ok-Ran Jeong Department of Software Gachon UniversityGyeonggi-do13120Korea

A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human *** is being actively conducted to cover a wide variety of knowledge,as it can be applied to applications that help ***,existing researches are constructing knowledge graphs without the time information that knowledge *** stored without time information becomes outdated over time,and in the future,the possibility of knowledge being false or meaningful changes is *** a result,they can’t reect information that changes dynamically,and they can’t accept information that has newly *** solve this problem,this paper proposes time-aware PolarisX,an automatically extended knowledge graph including time *** PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject,relation,and object from unstructured *** two application experiments,it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a ***,we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.

关键词： Machine learning natural language processing knowledge graph time-aware information extraction

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A time-aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a time-aware service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

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A Bias-Free time-aware PageRank Algorithm for Paper Ranking in Dynamic Citation Networks

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Intelligent Information Management 2022年第2期14卷 53-70页

作者： Moath Abu Dayeh Badie Sartawi Saeed Salah Department of Computer Science Al-Quds University Jerusalem Palestine

The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popularity metrics such as the citation count, h-index, and Impact Factor (IF). These adopted metrics cause a problem of bias in favor of older publications that took enough time to collect as many citations as possible. This paper focuses on solving the problem of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm;it is one of the main page ranking algorithms being widely used. The developed algorithm considers a newly suggested metric called the Citation Average rate of Change (CAC). time information such as publication date and the citation occurrence’s time are used along with citation data to calculate the new metric. The proposed ranking algorithm was tested on a dataset of scientific papers in the field of medical physics published in the Dimensions database from years 2005 to 2017. The experimental results have shown that the proposed ranking algorithm outperforms the PageRank algorithm in ranking scientific publications where 26 papers instead of only 14 were ranked among the top 100 papers of this dataset. In addition, there were no radical changes or unreasonable jump in the ranking process, i.e., the correlation rate between the results of the proposed ranking method and the original PageRank algorithm was 92% based on the Spearman correlation coefficient.

关键词： Bibliometric Citation Analysis Pagerank Algorithm Scientific Publications Metrics time-aware

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