Ontology Based Natural Language Queries Transformation into SPARQL Queries

被引:0
|
作者
Askar, Majid [2 ]
Algergawy, Alsayed [1 ]
Soliman, Taysir Hassan A. [2 ]
Koenig-Ries, Birgitta [1 ]
Sewisy, Adel A. [2 ]
机构
[1] Friedrich Schiller Univ Jena, Heinz Nixdorf Chair Distributed Informat Syst, Jena, Germany
[2] Assiut Univ, Fac Comp & Informat, Asyut, Egypt
来源
BALTIC JOURNAL OF MODERN COMPUTING | 2020年 / 8卷 / 04期
关键词
Knowledge management; OBDA; Natural Language; Query translation;
D O I
10.22364/bjmc.2020.8.4.14
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Ontology-based Data Access (OBDA) enables semantic access to a set of heterogamous data sources, supporting data sharing, exchanging, and integration across these data sources. In the OBDA scheme, normally formal query languages, such as SPARQL, are used to represent the user questions, which limits end users from defining their requests. To cope with this problem a layer that accepts the user request in her own language and transforms it into one of these formal languages has become a necessity. To this end, we introduce a new and interactive method that guides the user during the translation. The proposed approach makes use of the capabilities of natural language processing and the semantic information embedded in the domain ontology. Furthermore, the proposed approach considers user involvement during the translation process. To demonstrate the effectiveness, we implemented the proposed approach and validated it against a query benchmark assessing the query accuracy and efficiency.
引用
收藏
页码:719 / 731
页数:13
相关论文
共 50 条
  • [41] NATURAL LANGUAGE SEMANTIC CORPUS CONSTRUCTION BASED ON CLOUD SERVICE PLATFORM
    Wang, Su-Zhen
    Zhang, Qing-Chuan
    Zhang, Lu
    PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2, 2017, : 670 - 674
  • [42] Inferring a User's Propensity for Elaborative Thinking Based on Natural Language
    Chattaraman, Veena
    Kwon, Wi-Suk
    Green, Alexandra
    Gilbert, Juan E.
    ADVANCES IN NEUROERGONOMICS AND COGNITIVE ENGINEERING, 2019, 775 : 319 - 324
  • [43] Modular natural language interfaces to logic-based policy frameworks
    Perry, Jason
    Arkoudas, Konstantine
    Chiang, Jason
    Chadha, Ritu
    Apgar, Daniel
    Whittaker, Keith
    COMPUTER STANDARDS & INTERFACES, 2013, 35 (05) : 417 - 427
  • [44] Research on Natural Language Extraction Method Based on Deep Learning Technology
    Zhuang, Wei
    2021 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE BIG DATA AND INTELLIGENT SYSTEMS (HPBD&IS), 2021, : 88 - 93
  • [45] FromTo-CLIR™:: web-based natural language interface for cross-language information retrieval
    Kim, T
    Sim, CM
    Yuh, S
    Jung, H
    Kim, YK
    Choi, SK
    Park, DI
    Choi, KS
    INFORMATION PROCESSING & MANAGEMENT, 1999, 35 (04) : 559 - 586
  • [46] A Survey of Natural Language-Based Editing of Low-Code Applications Using Large Language Models
    Gorissen, Simon Cornelius
    Sauer, Stefan
    Beckmann, Wolf G.
    HUMAN-CENTERED SOFTWARE ENGINEERING, HCSE 2024, 2024, 14793 : 243 - 254
  • [47] Robust speech recognition in sports competition review based on natural language processing
    Wang, Penglong
    Feng, Yuhong
    Xi, Yongping
    Yang, Shengdong
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2023,
  • [48] Test of English vocabulary recognition based on natural language processing and corpus system
    Duan Longjiang
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) : 7073 - 7084
  • [49] A natural language processing based technique for sentiment analysis of college english corpus
    Xu J.
    PeerJ Computer Science, 2023, 9
  • [50] An Online Subject-Based Spam Filter Using Natural Language Features
    Lee, Chih-Ning
    Chen, Yi-Ruei
    Tzeng, Wen-Guey
    2017 IEEE CONFERENCE ON DEPENDABLE AND SECURE COMPUTING, 2017, : 479 - 484