融合知识表示的关系型数据库操作框架

doi:10.11896/jsjkx.211200030

摘要/Abstract

摘要： 关系型数据库指采用关系模型来组织数据的数据库,需要使用结构化查询语言(SQL)来操作其中的数据,面向应用时只能按照开发者设定好的程序规则进行数据库操作,程序规则的修改和新增过程繁琐且需要一定专业度,对普通用户不友好。为提高关系型数据库应用的扩展性和普适性,利用知识表示理论对数据库操作相关知识进行概念建模,使用框架结合规则的知识表示法建立数据库操作的一般化范式,基于关系型数据库的操作特点,结合关系模型及结构化查询语句的语法语义研究设计推理算法,将面向用户的数据库操作指令抽象成逻辑符号,并将事物之间的内在联系抽象为逻辑符号间的规则约束,使用求解系统根据逻辑符号表示的规则约束进行问题求解。基于上述理论及算法,设计并实现一种融合知识表示的关系型数据库操作框架,将用户输入转化成数据库操作语句,实现数据库系统操作。由应用实例可知,所提操作框架能在与关系型数据库友好兼容的基础上嵌入应用系统,程序规则易扩展,应用系统的使用和更新维护难度低,自适应性强,可以为用户提供更加灵活智能的数据库操作管控服务。

关键词: 知识表征, 知识推理, 关系型数据库

Abstract: Relational database refers to the database that uses relational model to organize data.It needs to use structured query language(SQL) to operate the data.When facing the application,it can only operate the database according to the program rules set by the developer.The process of modifying and adding program rules is cumbersome and requires a certain degree of professionalism,which is not friendly to ordinary users.In order to improve the expansion and universality of the application of rela-tional database,this paper uses the knowledge representation theory to model the knowledge related to database operation,uses the knowledge representation method of framework combined with rules to establish the general paradigm of database operation,and studies and designs the reasoning algorithm based on the operation characteristics of relational database and the syntax and semantics of relational model and structured query sentences.The user oriented database operation related things are abstracted into logical symbols,and the internal relationship between them is abstracted into the rule constraints between logical symbols.The problem is solved according to the rule constraints represented by logical symbols using the solving system.Based on the above theories and algorithms,a relational database operation framework integrating knowledge representation is designed and implemented,user input is converted into database operation statements to realize database system operation.It can be seen from the application example that the proposed operation framework can be embedded into the application system on the basis of friendly compatibility with relational database.The program rules are easy to expand,the application system has low difficulty in use,update and maintenance,and has strong self adaptability.It can provide users with more flexible and intelligent database ope-ration management and control services.

Key words: Knowledge representation, Knowledge reasoning, Relational database

中图分类号:

TP311

姜宗林, 李志军, 顾海军. 融合知识表示的关系型数据库操作框架[J]. 计算机科学, 2022, 49(11A): 211200030-9. https://doi.org/10.11896/jsjkx.211200030

JIANG Zong-lin, LI Zhi-jun, GU Hai-jun. Intelligent Operation Framework for Relational Database Application[J]. Computer Science, 2022, 49(11A): 211200030-9. https://doi.org/10.11896/jsjkx.211200030

参考文献

[1]WINIWARTER W,KAMBAYASHI Y.Natural Language In-terfaces to Databases－An Introduction[J].Proceedings of the Workshop on Computational Natural Language Learning,1997,10:125-135.
[2]GIORDANI A,MOSCHITTI A.Generating SQL queries using natural language syntactic dependencies and metadata[C]//International Conference on Application of Natural Language to Information Systems.Berlin:Springer,2012:164-170.
[3]HALLETT C.Generic querying of relational databases usingnatural language generation techniques[C]//Proceedings of the Fourth International Natural Language Generation Conference.2006:95-102.
[4]WOODS W A,KAPLAN R.Lunar rocks in natural English:Explorations in natural language question answering[J].Linguistic Structures Processing,1977,5:521-569.
[5]GREEN B F,WOLF A K,CHOMSKY C,et al.Baseball:an automatic question—answerer[C]//Proceedings of the Western Joint Computer Conference.Los Angeles:ACM,1961:219-224.
[6]GROSZ B J,JONES K S,WEBBER B L.Readings in natural language processing[R].SRI International,Menlo Park,CA(United States),1986.
[7]POPESCU A M,ARMANASU A,ETZIONI O,et al.Modern natural language interfaces to databases:Composing statistical parsing with semantic tractability[C]//Proceedings of the 20th International Conference on Computational Linguistics(CO-LING 2004).2004:141-147.
[8]BAIK C,JAGADISH H V,LI Y.Bridging the semantic gap with SQL query logs in natural language interfaces to databases[C]//2019 IEEE 35th International Conference on Data Engineering(ICDE).IEEE,2019:374-385.
[9]SAHA D,FLORATOU A,SANKARANARAYANAN K,et al.ATHENA:an ontology-driven system for natural language querying over relational data stores[C]//Proceedings of the VLDB Endowment.2016:1209-1220.
[10]YAGHMAZADEH N,WANG Y,DILLIG I,et al.SQLizer:query synthesis from natural language[J].Proceedings of the ACM on Programming Languages,2017,1(OOPSLA):1-26.
[11]SHAH A,PAREEK J,PATEL H,et al.NLKBIDB-Natural language and keyword based interface to database[C]//2013 International Conference on Advances in Computing,Communications and Informatics(ICACCI).IEEE,2013:1569-1576.
[12]LI F,JAGADISH H V.Constructing an interactive natural language interface for relational databases[J].Proceedings of the VLDB Endowment,2014,8(1):73-84.
[13]PAN X,XU S H,CAI X R,et al.Overview of database natural language interface based on deep learning [J].Computer Research and Development,2021,58(9):1925-1950.
[14]WEI Y D.Adaptive knowledge representation and reasoning of artificial intelligence [J].Journal of Shanghai Normal University(Pilosophy and Social Sciences Edition),2019,48(1):65-75.
[15]RICCA F,DIMASI A,GRASSO G,et al.A Logic-Based System for E-Tourism[J].Fundamenta Informatica,2010,105(1/2):35-55.
[16]RICCA F,GRASSO G,ALVIANO M,et al.Team-Building with Answer Set Programming in the Gioia-Tauro Seaport[J].Theory and Practice of Logic Programming,2012,12(3):361-381.
[17]GENÇAY E,SCHÜLLER P,ERDEM E.Applications of non-monotonic reasoning to automotive product configuration using answer set programming[J].Journal of Intelligent Manufactu-ring,2019,30:1407-1422.
[18]CANIUPAN M,BERTOSSI L.The Consistency ExtractorSystem:Querying Inconsistent Databases Using Answer Set Programs[C]//Scalable Uncertainty Management(SUM 2007).Lecture Notes in Computer Science,Berlin:Springer,2007.
[19]LEONE N,RICCA F.Answer Set Programming:A Tour from the Basics to Advanced Development Tools and Industrial Applications[M]//Reasoning Web.Web Logic Rules,2015:308-326.
[20]HUANG W M,HARRIE L.Towards knowledge-based geovi-sualisation using semantic web technologies:a knowledge representation approach coupling ontologies and rules[J].International Journal of Digital Earth,2020,13(9):66-78.
[21]SHANG F H,LI X,GONG M.Research on production know-ledge representation and reasoning based on Fuzzy framework [J].Computer Technology and Development,2014,24(7):38-42.
[22]HU Q,TAO H,ZOU D Q,et al.Empirical research on commo-dity knowledge fusion based on user cognitive framework [J].Information Theory and Practice,2020,43(4):94-100.
[23]XU D,WANG H,WANG M.A conceptual model of persona-lized virtual learning environments[J].Expert Systems with Applications,2005,29(3):525-534.
[24]BOLLOJU N,SUGUMARAN V.A knowledge-based object mo-deling advisor for developing quality object models[J].Expert Systems with Applications,2012,39(3):2893-2906.
[25]ALARCÓN R H,CHUECO J R,GARCÍA J M P,et al.Fixture knowledge model development and implementation based on a functional design approach[J].Robotics and Computer-Inte-grated Manufacturing,2010,26(1):56-66.
[26]Technology-Cybernetics.Investigators at University of Sao Paulo Describe Findings in Cybernetics(MOO-MDP:An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning)[J].Journal of Engineering,2019,26(2):547-559.
[27]SHIUE W,LI S T,CHEN K J.A frame knowledge system for managing financial decision knowledge[J].Expert Systems with Applications,2008,35(3):1068-1079.
[28]GELFOND M,LEONE N.Logic programming and knowledge representation－The A-prolog perspective[J].Artificial Intelligence,2002,138:3-38.
[29]GEBSER M,SCHAUB T,THIELE S.GrinGo:A New Groun-der for Answer Set Programming[C]//International Conference on Logic Programming and Nonmonotonic Reasoning.2007.

相关文章 15

[1]	马瑞新, 李泽阳, 陈志奎, 赵亮. 知识图谱推理研究综述 Review of Reasoning on Knowledge Graph 计算机科学, 2022, 49(6A): 74-85. https://doi.org/10.11896/jsjkx.210100122
[2]	杨如涵, 戴毅茹, 王坚, 董津. 基于表示学习的工业领域人机物本体融合 Humans-Cyber-Physical Ontology Fusion of Industry Based on Representation Learning 计算机科学, 2021, 48(5): 190-196. https://doi.org/10.11896/jsjkx.200500023
[3]	杭婷婷, 冯钧, 陆佳民. 知识图谱构建技术:分类、调查和未来方向 Knowledge Graph Construction Techniques:Taxonomy,Survey and Future Directions 计算机科学, 2021, 48(2): 175-189. https://doi.org/10.11896/jsjkx.200700010
[4]	鄂海红, 韩鹏昊, 宋美娜. 关系型数据库向图数据库的转换方法 Conversion Method from Relational Database to Graph Database 计算机科学, 2021, 48(10): 140-144. https://doi.org/10.11896/jsjkx.201100073
[5]	赖欣, 曾纪炜. 几何类航空数据与关系型数据库映射转换研究 Study on Mapping Transformation from Geometric Aviation Data to Relational Database 计算机科学, 2020, 47(11A): 570-572. https://doi.org/10.11896/jsjkx.200400040
[6]	张春霞, 彭成, 罗妹秋, 牛振东. 数学课程知识图谱构建及其推理 Construction of Mathematics Course Knowledge Graph and Its Reasoning 计算机科学, 2020, 47(11A): 573-578. https://doi.org/10.11896/jsjkx.191200141
[7]	文习明,方良达,余泉,常亮,王驹. 多智能体模态逻辑系统KD45_n中的知识遗忘 Knowledge Forgetting in Multi-agent Modal Logic System KD45_n 计算机科学, 2019, 46(7): 195-205. https://doi.org/10.11896/j.issn.1002-137X.2019.07.030
[8]	李智星, 任诗雅, 王化明, 沈柯. 基于非结构化文本增强关联规则的知识推理方法 Knowledge Reasoning Method Based on Unstructured Text-enhanced Association Rules 计算机科学, 2019, 46(11): 209-215. https://doi.org/10.11896/jsjkx.181001939
[9]	潘明明,李丁丁,汤庸,刘海. 一种基于中间件的异构数据库融合访问方法及系统 Design and Implemention of Accessing Hybrid Database Systems Based on Middleware 计算机科学, 2018, 45(5): 163-167. https://doi.org/10.11896/j.issn.1002-137X.2018.05.027
[10]	杨德先,孙华,于炯,国冰磊. 一种基于MBRC值的关系型数据库负载能耗预测模型 Relational Database Energy Prediction Model Based on MBRC 计算机科学, 2017, 44(7): 161-166. https://doi.org/10.11896/j.issn.1002-137X.2017.07.029
[11]	徐凤生,于秀清,史开泉. 动态知识智能发现与属性逻辑动态关系 Intelligent Discovery of Dynamic Knowledges and Logic Dynamic Relation between their Attributes 计算机科学, 2015, 42(4): 160-165. https://doi.org/10.11896/j.issn.1002-137X.2015.04.032
[12]	邹丽,谭雪微,张云霞. 语言真值直觉模糊逻辑的知识推理 Knowledge Reasoning Based on Linguistic Truth-valued Intuitionstic Fuzzy Logic 计算机科学, 2014, 41(1): 134-137.
[13]	张敬谊,童庆,肖筱华. 用于应急指挥的知识推理技术研究 Research on Knowledge Reasoning Technology in Emergency Command System 计算机科学, 2013, 40(2): 261-264.
[14]	倪俊，陈晓苏，刘辉宇，李劲. 网络安全策略求精一致性检测和冲突消解机制的研究 Research on Network Security Policy Refinement Consistency of Detection and Conflict Resolution Mechanisms 计算机科学, 2011, 38(2): 32-37.
[15]	. 空间数据库管理系统VISTA的强制访问控制设计计算机科学, 2007, 34(10): 149-151.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed