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This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using \u201cManchu Clan Genealogy\u201d as the data source.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-05-2022-0265","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:31Z","timestamp":1687996831000},"page":"1977-1999","source":"Crossref","is-referenced-by-count":11,"title":["A framework of genealogy knowledge reasoning and visualization based on a\u00a0knowledge graph"],"prefix":"10.1108","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5445-4943","authenticated-orcid":false,"given":"Ruan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5742-4854","authenticated-orcid":false,"given":"Jun","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhui","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"issue":"10","key":"key2024110704502637400_ref001","doi-asserted-by":"publisher","DOI":"10.1142\/S021800142059034X","article-title":"Correction tower: A general embedding method of the error recognition for the knowledge graph correction","volume":"34","year":"2020","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"key2024110704502637400_ref002","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1109\/ICDE51399.2021.00085","article-title":"Property graph schema optimization for domain-specific knowledge graphs","year":"2021"},{"key":"key2024110704502637400_ref003","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107626","article-title":"SParseQA: sequential word reordering and parsing for answering complex natural language questions over knowledge graphs","volume":"235","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"key2024110704502637400_ref004","first-page":"33","article-title":"Pattern based knowledge base enrichment","year":"2013"},{"key":"key2024110704502637400_ref005","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.3115\/v1\/D14-1165","article-title":"Typed tensor decomposition of knowledge bases for relation extraction","year":"2014"},{"key":"key2024110704502637400_ref006","article-title":"Analyzing legal education mobile learner's behavior using deep learning under social media","year":"2022","journal-title":"Library Hi Tech"},{"key":"key2024110704502637400_ref007","doi-asserted-by":"crossref","unstructured":"Chen, D., Pei, S. and Quan, Z. 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