{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-terminological-ontology-learning","title":"Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling","arxiv_id":"1708.09025","date":"2017-08-29","proceeding":null,"authors":["Xiaofeng Zhu","Diego Klabjan","Patrick Bless"],"abstract":"In this paper, we present hierarchical relationbased latent Dirichlet\nallocation (hrLDA), a data-driven hierarchical topic model for extracting\nterminological ontologies from a large number of heterogeneous documents. In\ncontrast to traditional topic models, hrLDA relies on noun phrases instead of\nunigrams, considers syntax and document structures, and enriches topic\nhierarchies with topic relations. Through a series of experiments, we\ndemonstrate the superiority of hrLDA over existing topic models, especially for\nbuilding hierarchies. Furthermore, we illustrate the robustness of hrLDA in the\nsettings of noisy data sets, which are likely to occur in many practical\nscenarios. Our ontology evaluation results show that ontologies extracted from\nhrLDA are very competitive with the ontologies created by domain experts.","url_abs":"http://arxiv.org/abs/1708.09025v1","url_pdf":"http://arxiv.org/pdf/1708.09025v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-terminological-ontology-learning","repo_url":"https://github.com/XiaofengZhu/hrLDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}