Linked Data Quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO

被引:0
|
作者
Faerber, Michael [1 ]
Bartscherer, Frederic [1 ]
Menne, Carsten [1 ]
Rettinger, Achim [1 ]
机构
[1] Karlsruhe Inst Technol, Inst AIFB, D-76131 Karlsruhe, Germany
关键词
Knowledge Graph; Linked Data Quality; Data Quality Metrics; Comparison; DBpedia; Freebase; OpenCyc; Wikidata; YAGO; WEB;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, several noteworthy large, cross-domain, and openly available knowledge graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Although extensively in use, these KGs have not been subject to an in-depth comparison so far. In this survey, we provide data quality criteria according to which KGs can be analyzed and analyze and compare the above mentioned KGs. Furthermore, we propose a framework for finding the most suitable KG for a given setting.
引用
收藏
页数:53
相关论文
共 50 条
  • [1] Comparing DBpedia, Wikidata, and YAGO for Web Information Retrieval
    Pillai, Sini Govinda
    Soon, Lay-Ki
    Haw, Su-Cheng
    [J]. INTELLIGENT AND INTERACTIVE COMPUTING, 2019, 67 : 525 - 535
  • [2] KORE 50DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase
    Noullet, Kristian
    Mix, Rico
    Faerber, Michael
    [J]. PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), 2020, : 2389 - 2395
  • [3] From Queriability to Informativity, Assessing "Quality in Use" of DBpedia and YAGO
    Ruan, Tong
    Li, Yang
    Wang, Haofen
    Zhao, Liang
    [J]. SEMANTIC WEB: LATEST ADVANCES AND NEW DOMAINS, 2016, 9678 : 52 - 68
  • [4] One Knowledge Graph to Rule Them All? Analyzing the Differences Between DBpedia, YAGO, Wikidata & co.
    Ringler, Daniel
    Paulheim, Heiko
    [J]. KI 2017: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2017, 10505 : 366 - 372
  • [5] DBpedia FlexiFusion the Best of Wikipedia > Wikidata > Your Data
    Frey, Johannes
    Hofer, Marvin
    Obraczka, Daniel
    Lehmann, Jens
    Hellmann, Sebastian
    [J]. SEMANTIC WEB - ISWC 2019, PT II, 2019, 11779 : 96 - 112
  • [6] ACRyLIQ: Leveraging DBpedia for Adaptive Crowdsourcing in Linked Data Quality Assessment
    ul Hassan, Umair
    Zaveri, Amrapali
    Marx, Edgard
    Curry, Edward
    Lehmann, Jens
    [J]. KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT, EKAW 2016, 2016, 10024 : 681 - 696
  • [7] Detecting Linked Data quality issues via crowdsourcing: A DBpedia study
    Acosta, Maribel
    Zaveri, Amrapali
    Simperl, Elena
    Kontokostas, Dimitris
    Floeck, Fabian
    Lehmann, Jens
    [J]. SEMANTIC WEB, 2018, 9 (03) : 303 - 335
  • [8] Ranking the Linked Data: The Case of DBpedia
    Mirizzi, Roberto
    Ragone, Azzurra
    Di Noia, Tommaso
    Di Sciascio, Eugenio
    [J]. WEB ENGINEERING, 2010, 6189 : 337 - 354
  • [9] Introducing Wikidata to the Linked Data Web
    Erxleben, Fredo
    Guenther, Michael
    Kroetzsch, Markus
    Mendez, Julian
    Vrandecic, Denny
    [J]. SEMANTIC WEB - ISWC 2014, PT I, 2014, 8796 : 50 - 65
  • [10] Sustainable Linked Data Generation: The Case of DBpedia
    Maroy, Wouter
    Dimou, Anastasia
    Kontokostas, Dimitris
    De Meester, Ben
    Verborgh, Ruben
    Lehmann, Jens
    Mannens, Erik
    Hellmann, Sebastian
    [J]. SEMANTIC WEB - ISWC 2017, PT II, 2017, 10588 : 297 - 313