{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:58:01Z","timestamp":1781103481369,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,1]]},"abstract":"<p>The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple &lt;New_York, IsCapitalOf, America&gt; satisfies the constraints that the head entity \u201cNew_York\u201d is a city and the tail entity \u201cAmerica\u201d belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.<\/p>","DOI":"10.4018\/jdm.2021100104","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T11:56:16Z","timestamp":1632830176000},"page":"48-64","source":"Crossref","is-referenced-by-count":8,"title":["Low-Quality Error Detection for Noisy Knowledge Graphs"],"prefix":"10.4018","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-0956","authenticated-orcid":true,"given":"*Chenyang","family":"Bu","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory of Knowledge Engineering with Big Data, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingchen","family":"Yu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Hong","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["Journal of Database Management"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=289793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T13:55:41Z","timestamp":1651845341000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/JDM.2021100104"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2021,10,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,10]]}},"URL":"https:\/\/doi.org\/10.4018\/jdm.2021100104","relation":{},"ISSN":["1063-8016","1533-8010"],"issn-type":[{"value":"1063-8016","type":"print"},{"value":"1533-8010","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,1]]}}}