Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and...Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.展开更多
The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to th...The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.展开更多
Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new genera...Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.展开更多
随着学校对图书馆经费投入的不断增加,数字环境下图书馆的合理使用变得越来越重要,其表现在数字资源上要有较多的用户访问和下载.从成员馆对比、登录情况、检索情况和成本等角度统计分析了东华大学ISI Web of Knowledge数据库使用情况,...随着学校对图书馆经费投入的不断增加,数字环境下图书馆的合理使用变得越来越重要,其表现在数字资源上要有较多的用户访问和下载.从成员馆对比、登录情况、检索情况和成本等角度统计分析了东华大学ISI Web of Knowledge数据库使用情况,并分析讨论了读者群和多校区使用情况等,为图书馆电子资源订购提供有效依据.展开更多
研究Web of Knowledge数据库收录南阳师范学院作者论文的情况,为科学研究管理提供参考.通过Web ofKnowledge数据库,以在地址词段选用"Nanyang"或者"Nan Yang"作为检索词,在检索结果中选择"CHINA",在单位...研究Web of Knowledge数据库收录南阳师范学院作者论文的情况,为科学研究管理提供参考.通过Web ofKnowledge数据库,以在地址词段选用"Nanyang"或者"Nan Yang"作为检索词,在检索结果中选择"CHINA",在单位机构名称中选择"NAN YANG NORMAL UNIV"、"NANYANG NORMAL COLL"、NANYANG TEACHERS COLL"以及"NANYANGNORMAL UNIV"进行年度收录量、国际合作度、作者单位、被收录论文来源、作者以及被引频次等检索.结果表明1998年以来,Web of Knowledge数据库收录南阳师范学院作者论文234篇,其中被引频次≥1的论文有131篇,被引文献数量占被收录论文总数的55.98%.最高被引为27次.收录论文最多的作者是GUO Ying-chen,有30篇论文被收录;收录最多之年是2011年,有62篇论文被收录;合作最多的单位是河南师范大学(HENAN NORMAL UNIV),合作论文21篇;来源最多的期刊是《无机化学学报》(CHINESE JOURNAL OF INORGANIC CHEMISTRY),有17篇论文被收录;研究最多的学科是化学(CHEMISTRY),有98篇论文,其中无机化学与核化学(CHEMISTRY INORGANIC NUCLEAR)38篇.Web of Knowledge数据库收录南阳师范学院作者论文中有44%的论文未被引用,被引频次≤3次的论文有78篇,占被引文献总数的59.54%.因此在重视论文收录数量的同时,应该将提高论文质量列为未来的研究重点.展开更多
A database stores data in order to provide the user with information. However, how a database may achieve this is not always clear. The main reason for this seems that we, who are in the database community, have not f...A database stores data in order to provide the user with information. However, how a database may achieve this is not always clear. The main reason for this seems that we, who are in the database community, have not fully understood and therefore clearly defined the notion of “the information that data in a database carry”, in other words, “the information content of data”. As a result, databases’ capability is limited in terms of answering queries, especially, when users explore information beyond the scope of data stored in a database, the database normally cannot provide it. The underlying reason of the problem is that queries are answered based on a direct match between a query and data (up to aggregations of the data). We observe that this is because the information that data carry is seen as exactly the data per se. To tackle this problem, we propose the notion of information content inclusion relation, and show that it formulates the intuitive notion of the “information content of data” and then show how this notion may be used for the derivation of information from data in a database.展开更多
Objective To establish the knowledge graph of“disease-syndrome-symptom-method-formula”in Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)for reducing the fuzziness and uncertainty of data,and for laying a foun...Objective To establish the knowledge graph of“disease-syndrome-symptom-method-formula”in Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)for reducing the fuzziness and uncertainty of data,and for laying a foundation for later knowledge reasoning and its application.Methods Under the guidance of experts in the classical formula of traditional Chinese medicine(TCM),the method of“top-down as the main,bottom-up as the auxiliary”was adopted to carry out knowledge extraction,knowledge fusion,and knowledge storage from the five aspects of the disease,syndrome,symptom,method,and formula for the original text of Treatise on Febrile Diseases,and so the knowledge graph of Treatise on Febrile Diseases was constructed.On this basis,the knowledge structure query and the knowledge relevance query were realized in a visual manner.Results The knowledge graph of“disease-syndrome-symptom-method-formula”in the Treatise on Febrile Diseases was constructed,containing 6469 entities and 10911 relational triples,on which the query of entities and their relationships can be carried out and the query result can be visualized.Conclusion The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system,and improves the completeness and accuracy of the knowledge representation,and the connection between“disease-syndrome-symptom-treatment-formula”,which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.展开更多
BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA,...BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA, MA(maintenance analysis), FTA(fault tree analysis) and vulnerability analysis. These information and analysis results are obtained on the basis of the domain expert's experience and knowledge. Upon the basis of the summary of BDARA methods, this paper provides applied knowledge database, puts forward BDARA's integrated thinking, implementing methods, and the key technology for IBDARA knowledge database's development.展开更多
Within the framework of the Deep-time Digital Earth(DDE)project,thematic databases driven by scientific issues will have strong scientific vitality.In the field of sedimentology,thematic databases based on the current...Within the framework of the Deep-time Digital Earth(DDE)project,thematic databases driven by scientific issues will have strong scientific vitality.In the field of sedimentology,thematic databases based on the current unified sedimentary knowledge tree established by the Sedimentary Data Group(Fig.1),can solve specific scientific problems effectively and improve the scope and utility of the DDE platform significantly.展开更多
An integrated solution for discovery of literature information knowledge is proposed. The analytic model of literature Information model and discovery of literature information knowledge are illustrated. Practical ill...An integrated solution for discovery of literature information knowledge is proposed. The analytic model of literature Information model and discovery of literature information knowledge are illustrated. Practical illustrative example for discovery of literature information knowledge is given.展开更多
Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part c...Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.展开更多
A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system...A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system is promoted from data processing up to knowledge processing,and a practical method of how to develop expert system using the popular database developing tools is proposed.展开更多
It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in...It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.展开更多
Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the presen...Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed model in the Neo4j graph data repository,along with the results achieved,is presented as a proof of concept.Using scholarly research articles on dementia risk factors as a case study,automatic knowledge extraction,storage,intelligent search,and visual navigation are illustrated.The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors.Overall,this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.展开更多
This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matri...This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matrices, and the operation of fuzzy inference play important roles.A realistic set of 25 washers and nuts are employed to conduct extensive experiments and simulations.The investigation includes a complete demonstration of engineering design. The results obtained from this feasibility study are very encouraging indeed because they represent the lower bound with respect to performance, namely correctrecognition rate, of what fuzzy methodology can do. This lower bound shows high recognition rate even with noisy input patterns, robustness in terms of noise tolerance, and simplicity in hardware implementation. Possible future works are suggested in the conclusion.展开更多
基金supported by the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences(2021-I2M-1-033)the National Key Research and Development Program of China(2022YFF0711900).
文摘Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.
文摘The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.
文摘Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.
文摘随着学校对图书馆经费投入的不断增加,数字环境下图书馆的合理使用变得越来越重要,其表现在数字资源上要有较多的用户访问和下载.从成员馆对比、登录情况、检索情况和成本等角度统计分析了东华大学ISI Web of Knowledge数据库使用情况,并分析讨论了读者群和多校区使用情况等,为图书馆电子资源订购提供有效依据.
文摘研究Web of Knowledge数据库收录南阳师范学院作者论文的情况,为科学研究管理提供参考.通过Web ofKnowledge数据库,以在地址词段选用"Nanyang"或者"Nan Yang"作为检索词,在检索结果中选择"CHINA",在单位机构名称中选择"NAN YANG NORMAL UNIV"、"NANYANG NORMAL COLL"、NANYANG TEACHERS COLL"以及"NANYANGNORMAL UNIV"进行年度收录量、国际合作度、作者单位、被收录论文来源、作者以及被引频次等检索.结果表明1998年以来,Web of Knowledge数据库收录南阳师范学院作者论文234篇,其中被引频次≥1的论文有131篇,被引文献数量占被收录论文总数的55.98%.最高被引为27次.收录论文最多的作者是GUO Ying-chen,有30篇论文被收录;收录最多之年是2011年,有62篇论文被收录;合作最多的单位是河南师范大学(HENAN NORMAL UNIV),合作论文21篇;来源最多的期刊是《无机化学学报》(CHINESE JOURNAL OF INORGANIC CHEMISTRY),有17篇论文被收录;研究最多的学科是化学(CHEMISTRY),有98篇论文,其中无机化学与核化学(CHEMISTRY INORGANIC NUCLEAR)38篇.Web of Knowledge数据库收录南阳师范学院作者论文中有44%的论文未被引用,被引频次≤3次的论文有78篇,占被引文献总数的59.54%.因此在重视论文收录数量的同时,应该将提高论文质量列为未来的研究重点.
文摘A database stores data in order to provide the user with information. However, how a database may achieve this is not always clear. The main reason for this seems that we, who are in the database community, have not fully understood and therefore clearly defined the notion of “the information that data in a database carry”, in other words, “the information content of data”. As a result, databases’ capability is limited in terms of answering queries, especially, when users explore information beyond the scope of data stored in a database, the database normally cannot provide it. The underlying reason of the problem is that queries are answered based on a direct match between a query and data (up to aggregations of the data). We observe that this is because the information that data carry is seen as exactly the data per se. To tackle this problem, we propose the notion of information content inclusion relation, and show that it formulates the intuitive notion of the “information content of data” and then show how this notion may be used for the derivation of information from data in a database.
基金The Open Fund of Hunan University of Traditional Chinese Medicine for the First-Class Discipline of Traditional Chinese Medicine(2018ZYX66)the Science Research Project of Hunan Provincial Department of Education(20C1391)the Natural Science Foundation of Hunan Province(2020JJ4461)。
文摘Objective To establish the knowledge graph of“disease-syndrome-symptom-method-formula”in Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)for reducing the fuzziness and uncertainty of data,and for laying a foundation for later knowledge reasoning and its application.Methods Under the guidance of experts in the classical formula of traditional Chinese medicine(TCM),the method of“top-down as the main,bottom-up as the auxiliary”was adopted to carry out knowledge extraction,knowledge fusion,and knowledge storage from the five aspects of the disease,syndrome,symptom,method,and formula for the original text of Treatise on Febrile Diseases,and so the knowledge graph of Treatise on Febrile Diseases was constructed.On this basis,the knowledge structure query and the knowledge relevance query were realized in a visual manner.Results The knowledge graph of“disease-syndrome-symptom-method-formula”in the Treatise on Febrile Diseases was constructed,containing 6469 entities and 10911 relational triples,on which the query of entities and their relationships can be carried out and the query result can be visualized.Conclusion The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system,and improves the completeness and accuracy of the knowledge representation,and the connection between“disease-syndrome-symptom-treatment-formula”,which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.
文摘BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA, MA(maintenance analysis), FTA(fault tree analysis) and vulnerability analysis. These information and analysis results are obtained on the basis of the domain expert's experience and knowledge. Upon the basis of the summary of BDARA methods, this paper provides applied knowledge database, puts forward BDARA's integrated thinking, implementing methods, and the key technology for IBDARA knowledge database's development.
文摘Within the framework of the Deep-time Digital Earth(DDE)project,thematic databases driven by scientific issues will have strong scientific vitality.In the field of sedimentology,thematic databases based on the current unified sedimentary knowledge tree established by the Sedimentary Data Group(Fig.1),can solve specific scientific problems effectively and improve the scope and utility of the DDE platform significantly.
文摘An integrated solution for discovery of literature information knowledge is proposed. The analytic model of literature Information model and discovery of literature information knowledge are illustrated. Practical illustrative example for discovery of literature information knowledge is given.
文摘Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.
文摘A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system is promoted from data processing up to knowledge processing,and a practical method of how to develop expert system using the popular database developing tools is proposed.
文摘It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.
文摘Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed model in the Neo4j graph data repository,along with the results achieved,is presented as a proof of concept.Using scholarly research articles on dementia risk factors as a case study,automatic knowledge extraction,storage,intelligent search,and visual navigation are illustrated.The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors.Overall,this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.
文摘This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matrices, and the operation of fuzzy inference play important roles.A realistic set of 25 washers and nuts are employed to conduct extensive experiments and simulations.The investigation includes a complete demonstration of engineering design. The results obtained from this feasibility study are very encouraging indeed because they represent the lower bound with respect to performance, namely correctrecognition rate, of what fuzzy methodology can do. This lower bound shows high recognition rate even with noisy input patterns, robustness in terms of noise tolerance, and simplicity in hardware implementation. Possible future works are suggested in the conclusion.
