[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127294-en":3,"doc-seo-127294-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127294,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","SPIREX - Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning","Relation extraction from scientific literature aligned with a domain ontology is a major challenge in natural language processing, especially for precision medicine applications. Large language models (LLMs) offer new solutions, but extracted triples can suffer from errors such as hallucinations. SPIREX extends the SPIRES-based framework for extracting triples about RNA molecules by combining schema-constrained prompt formulation with graph machine learning over an RNA knowledge graph (RNA-KG).","SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning  \nEmanuele Cavalleri  \nUniversity of Milan, Italy [emanuele.cavalleri@unimi.it](emanuele.cavalleri@unimi.it)  \nMauricio Soto-Gomez  \nUniversity of Milan, Italy [mauricio.soto@unimi.it](mauricio.soto@unimi.it)  \nAli Pashaeibarough  \nUniversity of Milan, Italy [ali.pashaeibarough@unimi.it](ali.pashaeibarough@unimi.it)  \nDario Malchiodi University of Milan, Italy [dario.malchiodi@unimi.it](dario.malchiodi@unimi.it)  \nHarry Caufield  \nLawrence Berkeley National Lab, USA [jhc@lbl.gov](jhc@lbl.gov)  \nJustin Reese  \nLawrence Berkeley National Lab, USA [justaddcoffee@gmail.com](justaddcoffee@gmail.com)  \nChristopher J. Mungall  \nLawrence Berkeley National Lab, USA [CJMungall@lbl.gov](CJMungall@lbl.gov)  \nPeter N. Robinson  \nCharité University, Berlin, Germany [peter.robinson@bih-charite.de](peter.robinson@bih-charite.de)  \nElena Casiraghi University of Milan, Italy [elena.casiraghi@unimi.it](elena.casiraghi@unimi.it)  \nGiorgio Valentini University of Milan, Italy [giorgio.valentini@unimi.it](giorgio.valentini@unimi.it)  \nMarco Mesiti  \nUniversity of Milan, Italy marco.mesiti@unimi.it  \nABSTRACT  \nRelation extraction from scientific literature to align with a domain ontology is a well-known challenge in natural language processing, particularly critical in precision medicine. The advent of large language models (LLMs) has enabled the development of new and effective approaches to this problem. However, the extracted relations can be prone to problems (e.g., hallucination) that must be minimized. In this paper, we present the initial development of SPIREX, an extension of the SPIRES-based system designed to extract triples from scientific literature involving RNA molecules. Our system leverages schema constraints in the formulation of LLM promptsand utilizes graph machine learning on our RNA-based knowledge graph, RNA-KG, to assess the plausibility of the extracted triples. RNA-KG comprises more than 12.5M edges representing various types of relationships involving RNA molecules.  \nVLDB Workshop Reference Format:  \nEmanuele Cavalleri, Mauricio Soto-Gomez, Ali Pashaeibarough, Dario Malchiodi, Harry Caufield, Justin Reese, Christopher J. Mungall, Peter N. Robinson, Elena Casiraghi, Giorgio Valentini, and Marco Mesiti. SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning. VLDB 2024 Workshop: LLM+KG.  \nVLDB Workshop Artifact Availability:  \nThe experiments have been realized by using the following datasets: (schema, docs, and manual annotations) [https://zenodo.org/records/11393776](https://zenodo.org/records/11393776); (RNA  \nKG): [https://zenodo.org/doi/10.5281/zenodo.10078876](https://zenodo.org/doi/10.5281/zenodo.10078876) .  \nThis work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit [https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[ ](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[this license. For any use beyond those covered by this license](this license. For any use beyond those covered by this license), [obtain permission by](obtain permission by)[emailing info@vldb.org. Copyright](emailing info@vldb.org. Copyright) is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.  \nProceedings of the VLDB Endowment. ISSN 2150-8097 .  \n1 INTRODUCTION  \nRibonucleic acid (RNA) is essential in the central dogma of molecular biology, functioning as the intermediary between DNA and proteins, the fundamental building blocks of life. Beyond its traditional role in protein synthesis, RNA is involved in various cellular processes, including gene regulation and catalysis, emphasizing its critical importance in understanding the complexities of biological systems. RNA-KG [8] is an ontology-based knowledge ","cbCaicmmeMnNliRp","https://ap.wps.com/l/cbCaicmmeMnNliRp","pdf",1732451,2,1,11,"English","en",105,"# Abstract\n# Introduction\n## RNA knowledge graph (RNA-KG)\n## Motivation for relation extraction from biomedical text\n## Limitations of supervised RE and LLM hallucinations\n## Motivation for LLM-KG integration","[{\"question\":\"What problem does SPIREX address in biomedical NLP?\",\"answer\":\"SPIREX targets relation extraction from scientific literature so the extracted relations align with a domain ontology, a challenge that is crucial for precision medicine.\"},{\"question\":\"How does SPIREX use LLMs to extract RNA-related triples?\",\"answer\":\"It formulates LLM prompts using schema constraints to guide the extraction process toward ontology-compatible triples.\"},{\"question\":\"How does graph machine learning improve the quality of extracted relations?\",\"answer\":\"SPIREX uses graph machine learning on the RNA knowledge graph (RNA-KG) to assess the plausibility of the extracted triples, helping reduce issues like hallucination.\"}]","SPIREX - Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning | PDF",1785938152,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"spirex-improving-llm-based-relation-extraction-from-rna-focused-scientific-literature-using-graph-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/spirex-improving-llm-based-relation-extraction-from-rna-focused-scientific-literature-using-graph-machine-learning/127294/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does SPIREX address in biomedical NLP?","Question",{"text":76,"@type":77},"SPIREX targets relation extraction from scientific literature so the extracted relations align with a domain ontology, a challenge that is crucial for precision medicine.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does SPIREX use LLMs to extract RNA-related triples?",{"text":81,"@type":77},"It formulates LLM prompts using schema constraints to guide the extraction process toward ontology-compatible triples.",{"name":83,"@type":74,"acceptedAnswer":84},"How does graph machine learning improve the quality of extracted relations?",{"text":85,"@type":77},"SPIREX uses graph machine learning on the RNA knowledge graph (RNA-KG) to assess the plausibility of the extracted triples, helping reduce issues like hallucination.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]