A CHAOS BASED REVITALIZATION OF LARGE RELIABILITY KNOWLEDGE BASES

被引:1
|
作者
DOHNAL, M
KVAPILIK, M
DOHNALOVA, J
VYKYDAL, J
机构
[1] Applied Information Technologies, 664 41 Troubsko, Ostopovice
关键词
D O I
10.1016/0026-2714(93)90486-I
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An interdependence among individual knowledge items makes any development of large reliability knowledge bases difficult. The key problem is to maintain a reasonable level of internal consistency in spite of heterogeneity of different knowledge items. A fairly general and efficient formal tools are presented in [1, 2]. They have been used for evaluation of an internal (in)consistency (represented e.g. by a level of chaos) of industrial knowledge bases. The only precondition for their application is an ability to distinguish between specific and general fuzzy items, A realistic case study (control valves) is presented in details.
引用
收藏
页码:259 / 265
页数:7
相关论文
共 50 条
  • [41] Extracting large-scale knowledge bases from the web
    Kumar, R
    Raghavan, P
    Rajagopalan, S
    Tomkins, A
    PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, 1999, : 639 - 650
  • [42] Sigma: Simple Greedy Matching for Aligning Large Knowledge Bases
    Lacoste-Julien, Simon
    Palla, Konstantina
    Davies, Alex
    Kasneci, Gjergji
    Graepel, Thore
    Ghahramani, Zoubin
    19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), 2013, : 572 - 580
  • [43] A Scalable Problem-Solver for Large Knowledge-Bases
    Chaw, Shaw-Yi
    Barker, Ken
    Porter, Bruce
    Tecuci, Dan
    Yeh, Peter Z.
    ICTAI: 2009 21ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, 2009, : 461 - +
  • [44] Mining Rules with Constants from Large Scale Knowledge Bases
    Wang, Xuan
    Zhang, Jingjing
    Chen, Jinchuan
    Fan, Ju
    CONCEPTUAL MODELING, ER 2018, 2018, 11157 : 521 - 535
  • [45] Semantic refinement and error correction in large terminological knowledge bases
    Geller, J
    Gu, HY
    Perl, Y
    Halper, M
    DATA & KNOWLEDGE ENGINEERING, 2003, 45 (01) : 1 - 32
  • [46] Chaos, Complexity, and a Revitalization of Four-Field Anthropology?
    White, Andrew
    REVIEWS IN ANTHROPOLOGY, 2015, 44 (03) : 142 - 160
  • [47] Ontological Pathfinding: Mining First-Order Knowledge from Large Knowledge Bases
    Chen, Yang
    Goldberg, Sean
    Wang, Daisy Zhe
    Johri, Soumitra Siddharth
    SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2016, : 835 - 846
  • [48] A KNOWLEDGE REPRESENTATION LANGUAGE FOR LARGE KNOWLEDGE BASES AND INTELLIGENT INFORMATION-RETRIEVAL SYSTEMS
    ZARRI, GP
    INFORMATION PROCESSING & MANAGEMENT, 1990, 26 (03) : 349 - 370
  • [49] Hybrid Reasoning Over Large Knowledge Bases Using On-The-Fly Knowledge Extraction
    Stoilos, Giorgos
    Juric, Damir
    Wartak, Szymon
    Schulz, Claudia
    Khodadadi, Mohammad
    SEMANTIC WEB (ESWC 2020), 2020, 12123 : 69 - 85
  • [50] Verification of knowledge bases based on containment checking
    Levy, AY
    Rousset, MC
    PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, 1996, : 585 - 591