[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118422-en":3,"doc-seo-118422-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118422,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Effective Knowledge Extraction and Knowledge-Enhanced Machine Learning for Health - Thesis","This thesis explores knowledge extraction as a way to enhance machine learning, with an emphasis on healthcare applications. It proposes methods to derive structured knowledge from unstructured text using pre-trained language models and advanced text analysis. The research covers optimizing knowledge graph completion, measuring the efficiency and accuracy of extraction from text, and improving text summarization to strengthen extraction. It further applies extracted knowledge to predict healthcare outcomes and molecule properties, supporting future work in healthcare quality and drug discovery.","© 2024 Pengcheng Jiang  \nEFFECTIVE KNOWLEDGE EXTRACTION AND KNOWLEDGE-ENHANCED  \nMACHINE LEARNING FOR HEALTH  \nBY  \nPENGCHENG JIANG  \nTHESIS  \nSubmitted in partial fulfillment of the requirements  \nfor the degree of Master of Science in Computer Science  \nin the Graduate College of the  \nUniversity of Illinois Urbana-Champaign, 2024  \nUrbana, Illinois  \nAdviser:  \nProfessor Jimeng Sun  \nABSTRACT  \nThis work explores the frontier of knowledge extraction and its application in enhancing machine learning models, with a special focus on healthcare. Through innovative methodologies, it presents a novel approach to deriving structured knowledge from unstructured data, leveraging the power of pre-trained language models and sophisticated text analysis techniques. The work introduces groundbreaking strategies for optimizing knowledge graph completion tasks, evaluating the efficiency and accuracy of knowledge extraction from textual data, and revolutionizing text summarization to improve knowledge extraction processes. Furthermore, it delves into the application of this extracted knowledge in healthcare, demonstrating the potential of knowledge-enhanced machine learning in predicting healthcare outcomes and molecule properties with unprecedented precision. This research not only advances the field of knowledge extraction and machine learning but also opens up new avenues for future research and applications, particularly in enhancing the quality of healthcare and drug discovery. Through its innovative methodologies and significant findings, this thesis underscores the transformative potential of artificial intelligence in extracting and leveraging knowledge for scientific and medical advancements.  \nTo my parents and Grandpa Tie, whose love and support know no bounds.  \nACKNOWLEDGMENTS  \nI extend my deepest gratitude to my research advisors, Professor Jimeng Sun and Professor Jiawei Han from the Department of Computer Science at the University of Illinois at UrbanaChampaign. Their unwavering support, insightful guidance, and valuable advice have been pivotal in navigating my research journey. Their encouragement inspired me to explore meaningful research topics and to strive for excellence in my research outputs, which form the foundation of this thesis.  \nIn the sphere of personal support, my heartfelt appreciation goes to my mother, whose steadfast backing on a modest income has been a constant pillar of strength for me. I am profoundly grateful to my father, who, despite passing away ten years ago due to cancer, left a lasting legacy by introducing me to the digital world with my first personal computer. Equally, I owe a significant debt of gratitude to Grandpa Tie. Though we share no blood relation, he raised me until I was nine and instilled in me a deep fascination for the scientific and engineering world through his exceptional skills as an engineer.  \nThe research works were supported in part by US DARPA KAIROS Program No. FA8750- 19-2-1004 and INCAS Program No. HR001121C0165, National Science Foundation IIS-19- 56151, and the Molecule Maker Lab Institute: An AI Research Institutes program supported by NSF under Award No. 2019897, and the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) by NSF under Award No. 2118329, and NSF award SCH-2205289, SCH-2014438, and IIS-2034479 .  \nTABLE OF CONTENTS  \nCHAPTER 1 INTRODUCTION ............................ 1  \nCHAPTER 2 KNOWLEDGE EXTRACTION FROM LANGUAGE MODELS ... 3  \n2.1 Overview ...................................... 3  \n2.2 Related Work ................................... 5  \n2.3 Methodology-TagReal .............................. 6  \n2.4 Experiment .................................... 11  \n2.5 Summary and Future Work ........................... 16  \nCHAPTER 3 KNOWLEDGE EXTRACTION FROM TEXT & EVALUATION .. 17  \n3.1 Overview ...................................... 17  \n3.2 Preliminaries ................................... 19  \n3.3 Methodolog","cbCaiupqF0sdiFYq","https://ap.wps.com/l/cbCaiupqF0sdiFYq","pdf",7450282,1,167,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Knowledge Extraction from Language Models\n## Overview\n## Related Work\n## Methodology-TagReal\n## Experiment\n## Summary and Future Work\n# Chapter 3 Knowledge Extraction from Text & Evaluation\n## Overview\n## Preliminaries\n## Methodology-GenRES\n## Experiments\n## Related Work\n## Summary and Future Work\n# Chapter 4 Summarization for Knowledge Extraction\n## Overview\n## Related Work\n## Methodology-TriSum\n## Experiments\n## Summary and Future Work\n# Chapter 5 Healthcare Predictions with Knowledge\n## Overview\n## Related Work\n## Methodology-GraphCare\n## Experiments\n## Summary and Future Work\n# Chapter 6 Molecule Property Prediction with Knowledge\n## Overview\n## Related Works\n## Methodology-Gode\n## Experiments\n## Summary and Future Work\n# Chapter 7 Conclusion and Future Work\n# Appendix A Appendix for Chapter 2\n## Dataset Overview\n## Textual Pattern Mining\n## Re-Ranking Recalls from KGE Model\n## Computing Infrastructure & Budget\n## Link Prediction with Ensemble\n## Evaluation Metrics\n## Code Interpretation\n## Case Study\n## Re-Evaluation of Knowledge Graph Embedding Models","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To explore knowledge extraction methods and use the extracted structured knowledge to enhance machine learning models, especially for healthcare-related tasks.\"},{\"question\":\"How does the thesis obtain structured knowledge from text?\",\"answer\":\"It uses pre-trained language models and text analysis techniques to convert unstructured data into structured knowledge representations, supporting downstream learning and reasoning tasks.\"},{\"question\":\"Which healthcare and scientific prediction tasks are addressed?\",\"answer\":\"The thesis applies knowledge-enhanced learning to predict healthcare outcomes and to predict molecule properties, using knowledge to improve precision.\"}]","Effective Knowledge Extraction and Knowledge-Enhanced Machine Learning for Health - Thesis | PDF",1785683538,421,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"effective-knowledge-extraction-and-knowledge-enhanced-machine-learning-for-health-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/effective-knowledge-extraction-and-knowledge-enhanced-machine-learning-for-health-thesis/118422/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this thesis?","Question",{"text":75,"@type":76},"To explore knowledge extraction methods and use the extracted structured knowledge to enhance machine learning models, especially for healthcare-related tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis obtain structured knowledge from text?",{"text":80,"@type":76},"It uses pre-trained language models and text analysis techniques to convert unstructured data into structured knowledge representations, supporting downstream learning and reasoning tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"Which healthcare and scientific prediction tasks are addressed?",{"text":84,"@type":76},"The thesis applies knowledge-enhanced learning to predict healthcare outcomes and to predict molecule properties, using knowledge to improve precision.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]