The Knowledge Increase Estimation Framework for Ontology Integration on the Instance Level

被引：5
|
作者：
Kozierkiewicz-Hetmanska, Adrianna [1 ]
Pietranik, Marcin [1 ]
Hnatkowska, Bogumila [1 ]
机构：
[1] Wroclaw Univ Sci & Technol, Fac Comp Sci & Management, Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland
来源：
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2017, PT I | 2017年 / 10191卷
关键词：
D O I：
10.1007/978-3-319-54472-4_1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Integration of big collections of data is time and cost consuming process. It can be beneficial when the knowledge is distributed in different sources, or not - when different sources contain redundant information or, what worse, inconsistent information. Authors propose a formula to estimate knowledge increase after the process of ontology integration on the instance level. The validity of the formula was checked by a questionnaire and confirmed by a statistical study. The formula allows to estimate knowledge increase in objective manner.
引用
收藏
页码：3 / 12
页数：10
相关论文
共 50 条
  • [11] Ontology Versioning Driven by Instance Evolution in the τOWL Framework
    Brahmia, Zouhaier
    Grandi, Fabio
    Zekri, Abir
    Bouaziz, Rafik
    JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT, 2022, 21 (01)
  • [12] Software Engineering Ontology - the Instance Knowledge (Part I)
    Wongthongtham, P.
    Chang, E.
    Dillon, T.
    Sommerville, I
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2007, 7 (02): : 15 - 26
  • [13] Ontology Instance Linking: Towards Interlinked Knowledge Graphs
    Heflin, Jeff
    Song, Dezhao
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 4163 - 4169
  • [14] Research on Integration of Ontology-Based Product Knowledge: Framework, Schema and Algorithm
    Liu, Wenjian
    Jiang, Yang
    Jin, Tianguo
    2009 IEEE INTERNATIONAL WORKSHOP ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS, 2009, : 565 - 569
  • [15] Instance Level Classification Confidence Estimation
    Alasalmi, Tuomo
    Koskimaki, Heli
    Suutala, Jaakko
    Roning, Juha
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, (DCAI 2016), 2016, 474 : 275 - 282
  • [16] Object-Oriented Ontology Enterprise Architecture Framework Supporting Enterprise Knowledge Integration
    Chao, M. H.
    Trappey, A. J. C.
    Chen, Neil K. T.
    TRANSDISCIPLINARITY AND THE FUTURE OF ENGINEERING, 2022, 28 : 43 - 52
  • [17] OntoINT: A Framework for Ontology Integration Based on Entity Linking from Heterogeneous Knowledge Sources
    Manoj, N.
    Deepak, Gerard
    Santhanavijayan, A.
    Lecture Notes on Data Engineering and Communications Technologies, 2022, 111 : 27 - 35
  • [18] An Ontology and Integration Framework for Smart Communities
    Kinkar, Shishir
    Hennessy, Mark
    Ray, Steven
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2016, 16 (01)
  • [19] Ontology based framework for data integration
    Salguero, Alberto
    Araque, Francisco
    Delgado, Cecilia
    WSEAS Transactions on Information Science and Applications, 2008, 5 (06): : 953 - 962
  • [20] Scalability in Ontology Instance Matching of Large Semantic Knowledge base
    Aono, Masaki
    Seddiqui, Md. Hanif
    PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, 2010, : 378 - +
12345