{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:10:56Z","timestamp":1760058656303,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada (NSERC)","doi-asserted-by":"publisher","award":["2017-06245"],"award-info":[{"award-number":["2017-06245"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Hierarchy analysis of the knowledge graphs aims to discover the latent structure inherent in knowledge base data. Drawing inspiration from topic modeling, which identifies latent themes and content patterns in text corpora, our research seeks to adapt these analytical frameworks to the hierarchical exploration of knowledge graphs. Specifically, we adopt a non-parametric probabilistic model, the nested hierarchical Dirichlet process, to the field of knowledge graphs. This model discovers latent subject-specific distributions along paths within the tree. Consequently, the global tree can be viewed as a collection of local subtrees for each subject, allowing us to represent subtrees for each subject and reveal cross-thematic topics. We assess the efficacy of this model in analyzing the topics and word distributions that form the hierarchical structure of complex knowledge graphs. We quantitatively evaluate our model using four common datasets: Freebase, Wikidata, DBpedia, and WebRED, demonstrating that it outperforms the latest neural hierarchical clustering techniques such as TraCo, SawETM, and HyperMiner. Additionally, we provide a qualitative assessment of the induced subtree for a single subject.<\/jats:p>","DOI":"10.3390\/axioms14040300","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T09:41:11Z","timestamp":1744710071000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of Topic Hierarchy with Subtree Representation for Knowledge Graphs"],"prefix":"10.3390","volume":"14","author":[{"given":"Yujia","family":"Zhang","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada"}]},{"given":"Wenjie","family":"Xu","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada"}]},{"given":"Zheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Hongkong and Shanghai Banking Corporation (HSBC), Youyi Road, Shanghai 201999, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4783-0717","authenticated-orcid":false,"given":"Marek Z.","family":"Reformat","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada"},{"name":"Information Technology Institute, University of Social Sciences, 90-113 Lodz, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge graphs: Representation, acquisition, and applications","volume":"33","author":"Ji","year":"2021","journal-title":"IEEE Trans. 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