Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

被引:3
|
作者
Zhou, Jianhui [1 ]
Song, Xiaoxia [1 ]
Li, Yong [1 ]
Gao, Yun [1 ]
Zhang, Xulong [1 ]
机构
[1] Shanxi Datong Univ, Sch Comp & Network Engn, Datong 037009, Peoples R China
关键词
Ontologies; Organizations; Buildings; Semantics; Real-time systems; Adaptive filters; Knowledge engineering; Real-time ontology; multi-domain knowledge organization; ontology matching; ontology integration; knowledge engineering;
D O I
10.1109/ACCESS.2021.3076833
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multi-domain knowledge organization is an effective way of correlating cross-domain knowledge or intercommunicating between cross-domain knowledge systems. As a knowledge organization model, ontology is widely used in information and management systems. To organize multi-domain knowledge, ontologies in different domains correlate to each other directly or indirectly. Generally, matching and integrating ontologies of different domain into a large scale ontology is the common way of directly correlating, while building a top level ontology is the main method for indirectly correlating. As the scale of domain ontologies get larger and larger, both direct and indirect methods become more difficult and time-consuming. In order to improve the organization of multi-domain knowledge, this paper presents a novel ontology organization method to build real-time ontology by adaptive filter while user presenting requirements. Only the entities related to user requirements are integrated, while building a real-time ontology. Firstly, the method searches domain ontologies that are related to user requirements. Then sub-ontologies are extracted from search results by filter, and they are integrated into a new ontology under direction of filter, i.e. real-time ontology. Finally, four criteria are introduced to evaluate real-time ontology. The experiment results illuminate that real-time ontology perform excellently in accuracy, recall, correctness and especially time-consuming.
引用
收藏
页码:66486 / 66497
页数:12
相关论文
共 50 条
  • [1] A real-time image forensics scheme based on multi-domain learning
    Yang, Bin
    Li, Zhenyu
    Zhang, Tao
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2020, 17 (01) : 29 - 40
  • [2] A real-time image forensics scheme based on multi-domain learning
    Bin Yang
    Zhenyu Li
    Tao Zhang
    Journal of Real-Time Image Processing, 2020, 17 : 29 - 40
  • [3] Multi-Domain Real-Time Simulation of a Hybrid Bus
    Janardhan, K. S.
    Venugopal, Ravinder
    Zahir, Abdul
    Surendra, C.
    2014 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES), 2014,
  • [4] An environment for multi-domain ontology development and knowledge acquisition
    Si, JX
    Cao, CG
    Wang, H
    Gu, F
    Feng, QZ
    Zhang, CX
    Zeng, QT
    Tian, W
    Zheng, YF
    ENGINEERING AND DEPLOYMENT OF COOPERATIVE INFORMATION SYSTEMS, PROCEEDINGS, 2002, 2480 : 104 - 116
  • [5] Multi-domain ontology mapping based on semantics
    Shengli Song
    Xiang Zhang
    Guimin Qin
    Cluster Computing, 2017, 20 : 3379 - 3391
  • [6] Multi-domain ontology mapping based on semantics
    Song, Shengli
    Zhang, Xiang
    Qin, Guimin
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2017, 20 (04): : 3379 - 3391
  • [7] Real-time Communication for Multicore Systems with Multi-domain Ring Buses
    Bui, Bach D.
    Pellizzoni, Rodolfo
    Chivukula, Deepti K.
    Caccamo, Marco
    16TH IEEE INTERNATIONAL CONFERENCE ON EMBEDDED AND REAL-TIME COMPUTING SYSTEMS AND APPLICATIONS (RTCSA 2010), 2010, : 23 - 32
  • [8] A New Signal Capture Method Based on Real-time Multi-domain Trigger in Communication Analyzer
    Yan, Xiao
    Wang, Qian
    Qin, Kaiyu
    IEEE CIRCUITS AND SYSTEMS INTERNATIONAL CONFERENCE ON TESTING AND DIAGNOSIS, 2009, : 217 - +
  • [9] Rank web documents based on multi-domain ontology
    Liu J.
    Zhou M.
    Lin L.
    Kim H.-J.
    Wang J.
    J. Ambient Intell. Humanized Comput., 2 (1573-1582): : 1573 - 1582
  • [10] Uptrendz: API-Centric Real-Time Recommendations in Multi-domain Settings
    Lacic, Emanuel
    Duricic, Tomislav
    Fadljevic, Leon
    Theiler, Dieter
    Kowald, Dominik
    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III, 2023, 13982 : 255 - 261