Construction of Mass Cultural and Sports Activity Knowledge Graph based on the Fusion of MultiSource Data

Huanliang Sun, Yumeng Ma, Junling Liu
{"title":"Construction of Mass Cultural and Sports Activity Knowledge Graph based on the Fusion of MultiSource Data","authors":"Huanliang Sun, Yumeng Ma, Junling Liu","doi":"10.1109/ICKECS56523.2022.10060175","DOIUrl":null,"url":null,"abstract":"With the advent of the era of big data, people are increasingly demanding for queries of mass cultural and sports activities, which have been given high attention. However, due to the challenges of such activities and the outstanding performance of knowledge graphs in recent years, in this paper we solve the problem from the perspective of knowledge graph. We first present a method to construct a knowledge graph of mass cultural and sports activities based on the fusion of multisource data for user activity query, then present a hierarchical modeling method combining ontology and taxonomy, design a BERT-based pipeline model to extract activity entities, solve the extraction of unstructured text data, and use a rule-based method to extract the relationship between activities and labels. Finally, the knowledge graph is constructed using the real activity data of Beijing region, and the multi-condition query of the activity is realized. The experimental results show that this method can effectively provide personalized activity query for users with different needs, and solve the problem of wide activity information and low matching between activities and users. A detailed experimental analysis was carried out to construct a data set using real activity data in Beijing, and the validity of the experiment was evaluated.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICKECS56523.2022.10060175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

With the advent of the era of big data, people are increasingly demanding for queries of mass cultural and sports activities, which have been given high attention. However, due to the challenges of such activities and the outstanding performance of knowledge graphs in recent years, in this paper we solve the problem from the perspective of knowledge graph. We first present a method to construct a knowledge graph of mass cultural and sports activities based on the fusion of multisource data for user activity query, then present a hierarchical modeling method combining ontology and taxonomy, design a BERT-based pipeline model to extract activity entities, solve the extraction of unstructured text data, and use a rule-based method to extract the relationship between activities and labels. Finally, the knowledge graph is constructed using the real activity data of Beijing region, and the multi-condition query of the activity is realized. The experimental results show that this method can effectively provide personalized activity query for users with different needs, and solve the problem of wide activity information and low matching between activities and users. A detailed experimental analysis was carried out to construct a data set using real activity data in Beijing, and the validity of the experiment was evaluated.
基于多源数据融合的大众文体活动知识图谱构建
随着大数据时代的到来,人们对大众文化体育活动的查询需求越来越高,受到高度关注。然而,由于此类活动的挑战以及近年来知识图谱的突出表现,本文从知识图谱的角度来解决这一问题。首先提出了基于多源数据融合的大众文体活动知识图谱构建方法,然后提出了本体与分类法相结合的分层建模方法,设计了基于bert的管道模型提取活动实体,解决了非结构化文本数据的提取问题,并采用基于规则的方法提取活动与标签之间的关系。最后,利用北京地区的真实活动数据构建了知识图谱,实现了活动的多条件查询。实验结果表明,该方法可以有效地为不同需求的用户提供个性化的活动查询,解决了活动信息宽泛、活动与用户匹配度低的问题。利用北京地区的实际活动数据构建数据集,进行了详细的实验分析,并对实验的有效性进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信