In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
In addition to soil samples, conventional soil maps, and experienced soil surveyors, text about soils(e.g., soil survey reports) is an important potential data source for extracting soil–environment relationships. Co...In addition to soil samples, conventional soil maps, and experienced soil surveyors, text about soils(e.g., soil survey reports) is an important potential data source for extracting soil–environment relationships. Considering that the words describing soil–environment relationships are often mixed with unrelated words, the first step is to extract the needed words and organize them in a structured way. This paper applies natural language processing(NLP) techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships. The method includes two steps:(1) construction of a knowledge frame and(2) information extraction using either a rule-based method or a statistic-based method for different types of information. For uniformly written text information, the rule-based approach was used to extract information. These types of variables include slope, elevation, accumulated temperature, annual mean temperature, annual precipitation, and frost-free period. For information contained in text written in diverse styles, the statistic-based method was adopted. These types of variables include landform and parent material. The soil species of China soil survey reports were selected as the experimental dataset. Precision(P), recall(R), and F1-measure(F1) were used to evaluate the performances of the method. For the rule-based method, the P values were 1, the R values were above 92%, and the F1 values were above 96% for all the involved variables. For the method based on the conditional random fields(CRFs), the P, R and F1 values for the parent material were, respectively, 84.15, 83.13, and 83.64%; the values for landform were 88.33, 76.81, and 82.17%, respectively. To explore the impact of text types on the performance of the CRFs-based method, CRFs models were trained and validated separately by the descriptive texts of soil types and typical profiles. For parent material, the maximum F1 value for the descriptive text of soil types was 90.7%, while the maximum F1 value for the descriptive text of soil profiles was only 75%. For landform, the maximum F1 value for the descriptive text of soil types was 85.33%, which was similar to that of the descriptive text of soil profiles(i.e., 85.71%). These results suggest that NLP techniques are effective for the extraction and structuration of soil–environment relationship information from a text data source.展开更多
There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the...There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.展开更多
The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extr...The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extractant molecules are investigated by linear regression analysis in the extraction of rare earths by various classes and structures of monoacidic organophosphorus extractants. The results indicate that in Linear free energy relationship formula 1gK(ex) = rho Sigma sigma(Phi) + psi Sigma upsilon + h generally follows for this kind of extraction systems. Accordingly, the quantitative structure-behaviour relationships of extractants are discussed. These relationships can be preliminarily applied to predict the 1gK(ex) values of rare earth extraction with definite structures of this class of extractants, and thus can provide some directions for the design of new RE extractants.展开更多
This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prom...This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.展开更多
Introduction The metal extraction is a process of complex formation by organic ligands with metal ions or corresponding ionic groups, that is to say, it is a multicomponent coordination process in the heterogeneous ph...Introduction The metal extraction is a process of complex formation by organic ligands with metal ions or corresponding ionic groups, that is to say, it is a multicomponent coordination process in the heterogeneous phase system. The property of extraction is chiefly dependent on the stability of metal complexes, which is closely related to both the structure of ligands and the nature of metal ions. Therefore, the physicochemical behaviour of the substituent in the ligand will influence the extractive ability of the extractant for a certain展开更多
Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take ...Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.展开更多
Poria is an important medicine for inducing diuresis to drain dampness from the middle energizer.However,the specific effective components and the potential mechanism of Poria remain largely unknown.To identify the ef...Poria is an important medicine for inducing diuresis to drain dampness from the middle energizer.However,the specific effective components and the potential mechanism of Poria remain largely unknown.To identify the effective components and the mechanism of Poria water extract(PWE)to treat dampness stagnancy due to spleen deficiency syndrome(DSSD),a rat model of DSSD was established through weight-loaded forced swimming,intragastric ice-water stimulation,humid living environment,and alternate-day fasting for 21 days.After 14 days of treatment with PWE,the results indicated that PWE increased fecal moisture percentage,urine output,D-xylose level and weight;amylase,albumin,and total protein levels;and the swimming time of rats with DSSD to different extents.Eleven highly related components were screened out using the spectrum-effect relationship and LC-MS.Mechanistic studies revealed that PWE significantly increased the expression of serum motilin(MTL),gastrin(GAS),ADCY5/6,p-PKAα/β/γcat,and phosphorylated cAMP-response element binding protein in the stomach,and AQP3 expression in the colon.Moreover,it decreased the levels of serum ADH,the expression of AQP3 and AQP4 in the stomach,AQP1 and AQP3 in the duodenum,and AQP4 in the colon.PWE induced diuresis to drain dampness in rats with DSSD.Eleven main effective components were identified in PWE.They exerted therapeutic effect by regulating the AC-cAMP-AQP signaling pathway in the stomach,MTL and GAS levels in the serum,AQP1 and AQP3 expression in the duodenum,and AQP3 and AQP4 expression in the colon.展开更多
煤炭是能源消费降碳的主力军,煤炭开发利用过程中产生的碳排放占全国碳排放总量的60%~70%,是我国完成碳减排任务的关键所在。煤炭开采利用碳排放治理技术知识图谱构建与应用聚焦煤炭开采利用碳排放治理技术,系统梳理出相关治理技术知识...煤炭是能源消费降碳的主力军,煤炭开发利用过程中产生的碳排放占全国碳排放总量的60%~70%,是我国完成碳减排任务的关键所在。煤炭开采利用碳排放治理技术知识图谱构建与应用聚焦煤炭开采利用碳排放治理技术,系统梳理出相关治理技术知识,在此基础上构建知识图谱,挖掘出不同技术间的内在联系、适用条件、实施效果及减排路径,为相关人员获取碳排放治理技术领域前沿知识提供支撑,推动煤炭行业向绿色低碳方向转型。一是广泛收集煤炭减排技术相关的专业书籍、术语字典、权威研究报告、中国知网核心期刊文献以及各类标准规范等,采用自底向上和自顶向下的混合构建法构建煤炭开采利用碳排放治理技术领域概念知识模型;二是运用BIO标注策略,并应用BERT+CRF(Bidirectional Encoder Representations from Transformers&Conditional Random Fields)模型,识别该领域实体;三是在实体识别基础上,应用BiLSTM-Attention模型进一步挖掘实体间关系,实现关系抽取;四是采用实体消歧和共指消解技术进行知识融合,消除数据中的矛盾与冗余信息;五是通过Neo4j图数据库存储实体与关系,基于上述结构化的方法与模型,由此完成煤炭开采利用碳排放治理技术领域知识图谱的构建。构建了涵盖排放特征、开采方式、利用方式和减碳技术四大类的煤炭开采利用碳排放治理技术领域知识概念模型,又将这四大类知识概念细分为12个子类,30个细类,形成了完整的概念分类体系。定义了10类命名实体及6种关系,基于提出的知识图谱构建组合方法与创新模型,抽取出12631个节点与32209个实体间关系,揭示了碳排放技术与排放特征、开采方式、利用方式之间的复杂关联,并根据已构建的煤炭开采利用碳排放治理技术领域的知识图谱,支持矿山企业选取相适配的减碳技术路径。随着煤炭行业低碳发展的场景拓展、数据的积累以及人工智能和大模型的发展,本研究将在多模态数据融合的基础上,优化图谱的构建方法,拓展图谱的应用范围,提高技术路径推荐的精准度。展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金supported by the National Natural Science Foundation of China (41431177 and 41601413)the National Basic Research Program of China (2015CB954102)+1 种基金the Natural Science Research Program of Jiangsu Province, China (BK20150975 and 14KJA170001)the Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China
文摘In addition to soil samples, conventional soil maps, and experienced soil surveyors, text about soils(e.g., soil survey reports) is an important potential data source for extracting soil–environment relationships. Considering that the words describing soil–environment relationships are often mixed with unrelated words, the first step is to extract the needed words and organize them in a structured way. This paper applies natural language processing(NLP) techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships. The method includes two steps:(1) construction of a knowledge frame and(2) information extraction using either a rule-based method or a statistic-based method for different types of information. For uniformly written text information, the rule-based approach was used to extract information. These types of variables include slope, elevation, accumulated temperature, annual mean temperature, annual precipitation, and frost-free period. For information contained in text written in diverse styles, the statistic-based method was adopted. These types of variables include landform and parent material. The soil species of China soil survey reports were selected as the experimental dataset. Precision(P), recall(R), and F1-measure(F1) were used to evaluate the performances of the method. For the rule-based method, the P values were 1, the R values were above 92%, and the F1 values were above 96% for all the involved variables. For the method based on the conditional random fields(CRFs), the P, R and F1 values for the parent material were, respectively, 84.15, 83.13, and 83.64%; the values for landform were 88.33, 76.81, and 82.17%, respectively. To explore the impact of text types on the performance of the CRFs-based method, CRFs models were trained and validated separately by the descriptive texts of soil types and typical profiles. For parent material, the maximum F1 value for the descriptive text of soil types was 90.7%, while the maximum F1 value for the descriptive text of soil profiles was only 75%. For landform, the maximum F1 value for the descriptive text of soil types was 85.33%, which was similar to that of the descriptive text of soil profiles(i.e., 85.71%). These results suggest that NLP techniques are effective for the extraction and structuration of soil–environment relationship information from a text data source.
文摘There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.
文摘The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extractant molecules are investigated by linear regression analysis in the extraction of rare earths by various classes and structures of monoacidic organophosphorus extractants. The results indicate that in Linear free energy relationship formula 1gK(ex) = rho Sigma sigma(Phi) + psi Sigma upsilon + h generally follows for this kind of extraction systems. Accordingly, the quantitative structure-behaviour relationships of extractants are discussed. These relationships can be preliminarily applied to predict the 1gK(ex) values of rare earth extraction with definite structures of this class of extractants, and thus can provide some directions for the design of new RE extractants.
文摘This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.
基金Supported by the National Natural Science Foundation of China
文摘Introduction The metal extraction is a process of complex formation by organic ligands with metal ions or corresponding ionic groups, that is to say, it is a multicomponent coordination process in the heterogeneous phase system. The property of extraction is chiefly dependent on the stability of metal complexes, which is closely related to both the structure of ligands and the nature of metal ions. Therefore, the physicochemical behaviour of the substituent in the ligand will influence the extractive ability of the extractant for a certain
基金supported by the National Natural Science Foundation of China (41431177 and 41422109)the Innovation Project of State Key Laboratory of Resources and Environmental Information System of China (O88RA20CYA)the Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China
文摘Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.
基金supported by the Key Technologies Research and Development Program(No.2017YFC1703004)Hubei Technological Innovation Special Fund(No.2019ACA121).
文摘Poria is an important medicine for inducing diuresis to drain dampness from the middle energizer.However,the specific effective components and the potential mechanism of Poria remain largely unknown.To identify the effective components and the mechanism of Poria water extract(PWE)to treat dampness stagnancy due to spleen deficiency syndrome(DSSD),a rat model of DSSD was established through weight-loaded forced swimming,intragastric ice-water stimulation,humid living environment,and alternate-day fasting for 21 days.After 14 days of treatment with PWE,the results indicated that PWE increased fecal moisture percentage,urine output,D-xylose level and weight;amylase,albumin,and total protein levels;and the swimming time of rats with DSSD to different extents.Eleven highly related components were screened out using the spectrum-effect relationship and LC-MS.Mechanistic studies revealed that PWE significantly increased the expression of serum motilin(MTL),gastrin(GAS),ADCY5/6,p-PKAα/β/γcat,and phosphorylated cAMP-response element binding protein in the stomach,and AQP3 expression in the colon.Moreover,it decreased the levels of serum ADH,the expression of AQP3 and AQP4 in the stomach,AQP1 and AQP3 in the duodenum,and AQP4 in the colon.PWE induced diuresis to drain dampness in rats with DSSD.Eleven main effective components were identified in PWE.They exerted therapeutic effect by regulating the AC-cAMP-AQP signaling pathway in the stomach,MTL and GAS levels in the serum,AQP1 and AQP3 expression in the duodenum,and AQP3 and AQP4 expression in the colon.
文摘煤炭是能源消费降碳的主力军,煤炭开发利用过程中产生的碳排放占全国碳排放总量的60%~70%,是我国完成碳减排任务的关键所在。煤炭开采利用碳排放治理技术知识图谱构建与应用聚焦煤炭开采利用碳排放治理技术,系统梳理出相关治理技术知识,在此基础上构建知识图谱,挖掘出不同技术间的内在联系、适用条件、实施效果及减排路径,为相关人员获取碳排放治理技术领域前沿知识提供支撑,推动煤炭行业向绿色低碳方向转型。一是广泛收集煤炭减排技术相关的专业书籍、术语字典、权威研究报告、中国知网核心期刊文献以及各类标准规范等,采用自底向上和自顶向下的混合构建法构建煤炭开采利用碳排放治理技术领域概念知识模型;二是运用BIO标注策略,并应用BERT+CRF(Bidirectional Encoder Representations from Transformers&Conditional Random Fields)模型,识别该领域实体;三是在实体识别基础上,应用BiLSTM-Attention模型进一步挖掘实体间关系,实现关系抽取;四是采用实体消歧和共指消解技术进行知识融合,消除数据中的矛盾与冗余信息;五是通过Neo4j图数据库存储实体与关系,基于上述结构化的方法与模型,由此完成煤炭开采利用碳排放治理技术领域知识图谱的构建。构建了涵盖排放特征、开采方式、利用方式和减碳技术四大类的煤炭开采利用碳排放治理技术领域知识概念模型,又将这四大类知识概念细分为12个子类,30个细类,形成了完整的概念分类体系。定义了10类命名实体及6种关系,基于提出的知识图谱构建组合方法与创新模型,抽取出12631个节点与32209个实体间关系,揭示了碳排放技术与排放特征、开采方式、利用方式之间的复杂关联,并根据已构建的煤炭开采利用碳排放治理技术领域的知识图谱,支持矿山企业选取相适配的减碳技术路径。随着煤炭行业低碳发展的场景拓展、数据的积累以及人工智能和大模型的发展,本研究将在多模态数据融合的基础上,优化图谱的构建方法,拓展图谱的应用范围,提高技术路径推荐的精准度。
