Example of a knowledge query.
<div><p>Background</p><p>The field of information extraction (IE) is currently exploring more versatile and efficient methods for minimization of reliance on extensive annotated datasets and integration of knowledge across tasks and domains.</p><p>Objective</p&...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852019886280146944 |
|---|---|
| author | Bei Li (90587) |
| author2 | Changbiao Li (14356569) Jianwei Sun (263070) Xu Zeng (402015) Xiaofan Chen (140368) Jing Zheng (65946) |
| author2_role | author author author author author |
| author_facet | Bei Li (90587) Changbiao Li (14356569) Jianwei Sun (263070) Xu Zeng (402015) Xiaofan Chen (140368) Jing Zheng (65946) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bei Li (90587) Changbiao Li (14356569) Jianwei Sun (263070) Xu Zeng (402015) Xiaofan Chen (140368) Jing Zheng (65946) |
| dc.date.none.fl_str_mv | 2025-05-29T17:48:20Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325082.g009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Example_of_a_knowledge_query_/29188518 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Marine Biology Cancer Science Policy Infectious Diseases Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified chinese medical expertise achieving commendable results extensive annotated datasets data stored within knowledge across tasks conducting knowledge mining jointly extracted using minimal annotated data generative extraction paradigm graph &# 8217 xlink "> incorporating pretrained language model medical knowledge graph advancing information extraction annotated data xlink "> knowledge graph information extraction graph databases knowledge integration web scraping tuning strategies thereby contributing storage techniques small amount optimized using enhanced representation efficient methods currently exploring course management cost savings construction process construction model approach addresses |
| dc.title.none.fl_str_mv | Example of a knowledge query. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Background</p><p>The field of information extraction (IE) is currently exploring more versatile and efficient methods for minimization of reliance on extensive annotated datasets and integration of knowledge across tasks and domains.</p><p>Objective</p><p>We aim to evaluate and refine the application of the universal IE (UIE) technology in the field of Chinese medical expertise in terms of processing accuracy and efficiency.</p><p>Methods</p><p>Our model integrates ontology modeling, web scraping, UIE, fine-tuning strategies, and graph databases, thereby covering knowledge modeling, extraction, and storage techniques. The Enhanced Representation through Knowledge Integration-UIE (ERNIE-UIE) model is fine-tuned and optimized using a small amount of annotated data. A medical knowledge graph is then constructed, followed by validating the graph and conducting knowledge mining on the data stored within it.</p><p>Results</p><p>Incorporating the characteristics of whole-course management, we implemented a comprehensive medical knowledge graph–construction model and methodology. Entities and relationships were jointly extracted using the pretrained language model, resulting in 8,525 entity data points and 9,522 triple data points. The accuracy of the knowledge graph was verified using graph algorithms.</p><p>Conclusion</p><p>We optimized the construction process of a Chinese medical knowledge graph with minimal annotated data by utilizing a generative extraction paradigm, validating the graph’s efficacy and achieving commendable results. This approach addresses the challenge of insufficient annotated training corpora in low-resource knowledge graph construction, thereby contributing to cost savings in the development of knowledge graphs.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_03b4c79a5c19d363e1e60fe046dd844f |
| identifier_str_mv | 10.1371/journal.pone.0325082.g009 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29188518 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Example of a knowledge query.Bei Li (90587)Changbiao Li (14356569)Jianwei Sun (263070)Xu Zeng (402015)Xiaofan Chen (140368)Jing Zheng (65946)Marine BiologyCancerScience PolicyInfectious DiseasesPlant BiologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedchinese medical expertiseachieving commendable resultsextensive annotated datasetsdata stored withinknowledge across tasksconducting knowledge miningjointly extracted usingminimal annotated datagenerative extraction paradigmgraph &# 8217xlink "> incorporatingpretrained language modelmedical knowledge graphadvancing information extractionannotated dataxlink ">knowledge graphinformation extractiongraph databasesknowledge integrationweb scrapingtuning strategiesthereby contributingstorage techniquessmall amountoptimized usingenhanced representationefficient methodscurrently exploringcourse managementcost savingsconstruction processconstruction modelapproach addresses<div><p>Background</p><p>The field of information extraction (IE) is currently exploring more versatile and efficient methods for minimization of reliance on extensive annotated datasets and integration of knowledge across tasks and domains.</p><p>Objective</p><p>We aim to evaluate and refine the application of the universal IE (UIE) technology in the field of Chinese medical expertise in terms of processing accuracy and efficiency.</p><p>Methods</p><p>Our model integrates ontology modeling, web scraping, UIE, fine-tuning strategies, and graph databases, thereby covering knowledge modeling, extraction, and storage techniques. The Enhanced Representation through Knowledge Integration-UIE (ERNIE-UIE) model is fine-tuned and optimized using a small amount of annotated data. A medical knowledge graph is then constructed, followed by validating the graph and conducting knowledge mining on the data stored within it.</p><p>Results</p><p>Incorporating the characteristics of whole-course management, we implemented a comprehensive medical knowledge graph–construction model and methodology. Entities and relationships were jointly extracted using the pretrained language model, resulting in 8,525 entity data points and 9,522 triple data points. The accuracy of the knowledge graph was verified using graph algorithms.</p><p>Conclusion</p><p>We optimized the construction process of a Chinese medical knowledge graph with minimal annotated data by utilizing a generative extraction paradigm, validating the graph’s efficacy and achieving commendable results. This approach addresses the challenge of insufficient annotated training corpora in low-resource knowledge graph construction, thereby contributing to cost savings in the development of knowledge graphs.</p></div>2025-05-29T17:48:20ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0325082.g009https://figshare.com/articles/figure/Example_of_a_knowledge_query_/29188518CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291885182025-05-29T17:48:20Z |
| spellingShingle | Example of a knowledge query. Bei Li (90587) Marine Biology Cancer Science Policy Infectious Diseases Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified chinese medical expertise achieving commendable results extensive annotated datasets data stored within knowledge across tasks conducting knowledge mining jointly extracted using minimal annotated data generative extraction paradigm graph &# 8217 xlink "> incorporating pretrained language model medical knowledge graph advancing information extraction annotated data xlink "> knowledge graph information extraction graph databases knowledge integration web scraping tuning strategies thereby contributing storage techniques small amount optimized using enhanced representation efficient methods currently exploring course management cost savings construction process construction model approach addresses |
| status_str | publishedVersion |
| title | Example of a knowledge query. |
| title_full | Example of a knowledge query. |
| title_fullStr | Example of a knowledge query. |
| title_full_unstemmed | Example of a knowledge query. |
| title_short | Example of a knowledge query. |
| title_sort | Example of a knowledge query. |
| topic | Marine Biology Cancer Science Policy Infectious Diseases Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified chinese medical expertise achieving commendable results extensive annotated datasets data stored within knowledge across tasks conducting knowledge mining jointly extracted using minimal annotated data generative extraction paradigm graph &# 8217 xlink "> incorporating pretrained language model medical knowledge graph advancing information extraction annotated data xlink "> knowledge graph information extraction graph databases knowledge integration web scraping tuning strategies thereby contributing storage techniques small amount optimized using enhanced representation efficient methods currently exploring course management cost savings construction process construction model approach addresses |