KNAML: A Knowledge Representation Language for distributed reasoning

被引:0
|
作者
Streeter, G [1 ]
Potter, A [1 ]
机构
[1] Sentar, Huntsville, AL 35816 USA
来源
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Knowledge Agent Mediation Language (KNAML) is designed for use in multi-agent reasoning systems. Like conceptual graphs, KNAML represents knowledge using concepts, relations, and graphs. Concepts and relations are linked to form graphs, and graphs may be nested within other graphs. Additional constructs are used to support distributed reasoning and ontological concision. KNAML treats ontologies as knowledge domains that happen to be of the ontology domain. It uses an ontology of ontologies to define the concept and relation types available in an ontology. KNAML knowledge resources are modular to facilitate rapid development and efficient inter-agent processing. KNAML supports ontological specification of an extensible set of knowledge modalities, such as workflows, decision trees, and graphs that reflect the processing specializations of various knowledge agents and supports multi-modal knowledge authoring. Implemented in Java, KNAML supports subsumption, unification, and binding operations required by the host multi-agent system to carry out knowledge discovery and synthesis.
引用
收藏
页码:361 / 374
页数:14
相关论文
共 50 条
  • [1] Knowledge representation and reasoning in (controlled) natural language
    Fuchs, NE
    [J]. CONCEPTUAL STRUCTURES: COMMON SEMANTICS FOR SHARING KNOWLEDGE, PROCEEDINGS, 2005, 3596 : 51 - 51
  • [2] A KNOWLEDGE REPRESENTATION LANGUAGE FOR NATURAL LANGUAGE PROCESSING, SIMULATION AND REASONING
    McShane, Marjorie
    Nirenburg, Sergei
    [J]. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING, 2012, 6 (01) : 3 - 23
  • [3] KNOWLEDGE REPRESENTATION AND REASONING
    LEVESQUE, HJ
    [J]. ANNUAL REVIEW OF COMPUTER SCIENCE, 1986, 1 : 255 - 287
  • [4] Representation and reasoning of context-dependant knowledge in distributed fuzzy ontologies
    Jiang, Yuncheng
    Tang, Yong
    Wang, Ju
    Tang, Suqin
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (08) : 6052 - 6060
  • [5] Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning
    Liu, Kangzheng
    Zhao, Feng
    Xu, Guandong
    Wang, Xianzhi
    Jin, Hai
    [J]. 2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2022, : 279 - 288
  • [6] A Reasoning System for Fuzzy Distributed Knowledge Representation in Multi-Agent Systems
    Maruyama, Yoshihiro
    [J]. IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE), 2021,
  • [7] Knowledge representation and analogical reasoning
    Klix, F
    Bachmann, T
    [J]. INTERNATIONAL JOURNAL OF PSYCHOLOGY, 1996, 31 (3-4) : 3184 - 3184
  • [8] Knowledge graph representation and reasoning
    Cambria, Erik
    Ji, Shaoxiong
    Pan, Shirui
    Yu, Philip S.
    [J]. Neurocomputing, 2021, 461 : 494 - 496
  • [9] THE BASICS OF KNOWLEDGE REPRESENTATION AND REASONING
    BRACHMAN, RJ
    [J]. AT&T TECHNICAL JOURNAL, 1988, 67 (01): : 7 - 24
  • [10] Knowledge graph representation and reasoning
    Cambria, Erik
    Ji, Shaoxiong
    Pan, Shirui
    Yu, Philip S.
    [J]. NEUROCOMPUTING, 2021, 461 : 494 - 496