Knowledge extraction system and knowledge extraction method based on large language model algorithm

The invention discloses a knowledge extraction system and method based on a large language model algorithm, and the system comprises a data collection and cleaning module, an entity and relation extraction module, an entity disambiguation and alignment module, and a knowledge storage module. The kno...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: JIN YAOGUANG, WANG CONGRUI, WANG GANG, HU YAQING, GU SHANZHI, WU YAO, GAO SHAOBO, CHANG BINGGUO
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator JIN YAOGUANG
WANG CONGRUI
WANG GANG
HU YAQING
GU SHANZHI
WU YAO
GAO SHAOBO
CHANG BINGGUO
description The invention discloses a knowledge extraction system and method based on a large language model algorithm, and the system comprises a data collection and cleaning module, an entity and relation extraction module, an entity disambiguation and alignment module, and a knowledge storage module. The knowledge extraction system is arranged on a data collection layer, a data filtering layer, a knowledge extraction layer, a knowledge disambiguation layer and a knowledge storage layer respectively and used for achieving knowledge extraction and dynamically evaluating actual requirements of an external knowledge base in the knowledge extraction process, the knowledge extraction efficiency can be improved, and an extraction result with higher quality can be output. 本申请公开了一种基于大语言模型算法的知识抽取系统以及知识抽取方法,其中,基于大语言模型算法的知识抽取系统包括数据收集与清洗模块、实体与关系抽取模块、实体消歧与对齐模块、知识存储模块,分别设置在数据收集层、数据过滤层、知识抽取层、知识消岐层与知识存储层,用于实现知识抽取,动态地评估在知识抽取过程中对外部知识库的实际需求,可以提高知识抽取的效率,输出更高质量的抽取结果。
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118551840A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118551840A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118551840A3</originalsourceid><addsrcrecordid>eNqNijEKAjEQRbexEPUO4wEEgy5sK4siCFb2y5iM2cVJZklG1NubwtLC6v3Hf9PKnqI8mZwnoJcmtDpIhPzOSgEwOrj_-gNpLw6umMlBccZUAsboH1hGEEcMyF7SoH2YV5MbcqbFl7Nqedhf2uOKRukoj2gpknbt2Zimrk2zXe82_zQf-WY_Cg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Knowledge extraction system and knowledge extraction method based on large language model algorithm</title><source>esp@cenet</source><creator>JIN YAOGUANG ; WANG CONGRUI ; WANG GANG ; HU YAQING ; GU SHANZHI ; WU YAO ; GAO SHAOBO ; CHANG BINGGUO</creator><creatorcontrib>JIN YAOGUANG ; WANG CONGRUI ; WANG GANG ; HU YAQING ; GU SHANZHI ; WU YAO ; GAO SHAOBO ; CHANG BINGGUO</creatorcontrib><description>The invention discloses a knowledge extraction system and method based on a large language model algorithm, and the system comprises a data collection and cleaning module, an entity and relation extraction module, an entity disambiguation and alignment module, and a knowledge storage module. The knowledge extraction system is arranged on a data collection layer, a data filtering layer, a knowledge extraction layer, a knowledge disambiguation layer and a knowledge storage layer respectively and used for achieving knowledge extraction and dynamically evaluating actual requirements of an external knowledge base in the knowledge extraction process, the knowledge extraction efficiency can be improved, and an extraction result with higher quality can be output. 本申请公开了一种基于大语言模型算法的知识抽取系统以及知识抽取方法,其中,基于大语言模型算法的知识抽取系统包括数据收集与清洗模块、实体与关系抽取模块、实体消歧与对齐模块、知识存储模块,分别设置在数据收集层、数据过滤层、知识抽取层、知识消岐层与知识存储层,用于实现知识抽取,动态地评估在知识抽取过程中对外部知识库的实际需求,可以提高知识抽取的效率,输出更高质量的抽取结果。</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240827&amp;DB=EPODOC&amp;CC=CN&amp;NR=118551840A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240827&amp;DB=EPODOC&amp;CC=CN&amp;NR=118551840A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>JIN YAOGUANG</creatorcontrib><creatorcontrib>WANG CONGRUI</creatorcontrib><creatorcontrib>WANG GANG</creatorcontrib><creatorcontrib>HU YAQING</creatorcontrib><creatorcontrib>GU SHANZHI</creatorcontrib><creatorcontrib>WU YAO</creatorcontrib><creatorcontrib>GAO SHAOBO</creatorcontrib><creatorcontrib>CHANG BINGGUO</creatorcontrib><title>Knowledge extraction system and knowledge extraction method based on large language model algorithm</title><description>The invention discloses a knowledge extraction system and method based on a large language model algorithm, and the system comprises a data collection and cleaning module, an entity and relation extraction module, an entity disambiguation and alignment module, and a knowledge storage module. The knowledge extraction system is arranged on a data collection layer, a data filtering layer, a knowledge extraction layer, a knowledge disambiguation layer and a knowledge storage layer respectively and used for achieving knowledge extraction and dynamically evaluating actual requirements of an external knowledge base in the knowledge extraction process, the knowledge extraction efficiency can be improved, and an extraction result with higher quality can be output. 本申请公开了一种基于大语言模型算法的知识抽取系统以及知识抽取方法,其中,基于大语言模型算法的知识抽取系统包括数据收集与清洗模块、实体与关系抽取模块、实体消歧与对齐模块、知识存储模块,分别设置在数据收集层、数据过滤层、知识抽取层、知识消岐层与知识存储层,用于实现知识抽取,动态地评估在知识抽取过程中对外部知识库的实际需求,可以提高知识抽取的效率,输出更高质量的抽取结果。</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNijEKAjEQRbexEPUO4wEEgy5sK4siCFb2y5iM2cVJZklG1NubwtLC6v3Hf9PKnqI8mZwnoJcmtDpIhPzOSgEwOrj_-gNpLw6umMlBccZUAsboH1hGEEcMyF7SoH2YV5MbcqbFl7Nqedhf2uOKRukoj2gpknbt2Zimrk2zXe82_zQf-WY_Cg</recordid><startdate>20240827</startdate><enddate>20240827</enddate><creator>JIN YAOGUANG</creator><creator>WANG CONGRUI</creator><creator>WANG GANG</creator><creator>HU YAQING</creator><creator>GU SHANZHI</creator><creator>WU YAO</creator><creator>GAO SHAOBO</creator><creator>CHANG BINGGUO</creator><scope>EVB</scope></search><sort><creationdate>20240827</creationdate><title>Knowledge extraction system and knowledge extraction method based on large language model algorithm</title><author>JIN YAOGUANG ; WANG CONGRUI ; WANG GANG ; HU YAQING ; GU SHANZHI ; WU YAO ; GAO SHAOBO ; CHANG BINGGUO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118551840A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>JIN YAOGUANG</creatorcontrib><creatorcontrib>WANG CONGRUI</creatorcontrib><creatorcontrib>WANG GANG</creatorcontrib><creatorcontrib>HU YAQING</creatorcontrib><creatorcontrib>GU SHANZHI</creatorcontrib><creatorcontrib>WU YAO</creatorcontrib><creatorcontrib>GAO SHAOBO</creatorcontrib><creatorcontrib>CHANG BINGGUO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>JIN YAOGUANG</au><au>WANG CONGRUI</au><au>WANG GANG</au><au>HU YAQING</au><au>GU SHANZHI</au><au>WU YAO</au><au>GAO SHAOBO</au><au>CHANG BINGGUO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Knowledge extraction system and knowledge extraction method based on large language model algorithm</title><date>2024-08-27</date><risdate>2024</risdate><abstract>The invention discloses a knowledge extraction system and method based on a large language model algorithm, and the system comprises a data collection and cleaning module, an entity and relation extraction module, an entity disambiguation and alignment module, and a knowledge storage module. The knowledge extraction system is arranged on a data collection layer, a data filtering layer, a knowledge extraction layer, a knowledge disambiguation layer and a knowledge storage layer respectively and used for achieving knowledge extraction and dynamically evaluating actual requirements of an external knowledge base in the knowledge extraction process, the knowledge extraction efficiency can be improved, and an extraction result with higher quality can be output. 本申请公开了一种基于大语言模型算法的知识抽取系统以及知识抽取方法,其中,基于大语言模型算法的知识抽取系统包括数据收集与清洗模块、实体与关系抽取模块、实体消歧与对齐模块、知识存储模块,分别设置在数据收集层、数据过滤层、知识抽取层、知识消岐层与知识存储层,用于实现知识抽取,动态地评估在知识抽取过程中对外部知识库的实际需求,可以提高知识抽取的效率,输出更高质量的抽取结果。</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN118551840A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Knowledge extraction system and knowledge extraction method based on large language model algorithm
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T12%3A37%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=JIN%20YAOGUANG&rft.date=2024-08-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118551840A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true