A survey of formalisms for representing and reasoning with scientific knowledge

被引:14
|
作者
Hunter, Anthony [1 ]
Liu, Weiru [2 ]
机构
[1] UCL, Dept Comp Sci, London WC1E 6BT, England
[2] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5BN, Antrim, North Ireland
基金
英国工程与自然科学研究理事会;
关键词
INCOMPLETE STATISTICAL INFORMATION; LOGIC; ARGUMENTATION; SYSTEM; ONTOLOGIES; NETWORKS; INTEGRATION; MODELS;
D O I
10.1017/S0269888910000019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.
引用
收藏
页码:199 / 222
页数:24
相关论文
共 50 条
  • [1] Formalisms of Representing Knowledge
    Patel, Archana
    Jain, Sarika
    6TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS, 2018, 125 : 542 - 549
  • [2] Representing, manipulating and reasoning with geographic semantics within a knowledge framework
    O'Brien, J
    Gahegan, M
    DEVELOPMENTS IN SPATIAL DATA HANDLING, 2005, : 585 - 603
  • [3] Abductive reasoning and the formation of scientific knowledge within nursing research
    Raholm, Maj-Britt
    NURSING PHILOSOPHY, 2010, 11 (04) : 260 - 270
  • [4] Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey
    Delong, Lauren Nicole
    Mir, Ramon Fernandez
    Fleuriot, Jacques D.
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024,
  • [5] A survey of imperatives and action representation formalisms
    Bama Srinivasan
    Ranjani Parthasarathi
    Artificial Intelligence Review, 2017, 48 : 263 - 297
  • [6] A survey of imperatives and action representation formalisms
    Srinivasan, Bama
    Parthasarathi, Ranjani
    ARTIFICIAL INTELLIGENCE REVIEW, 2017, 48 (02) : 263 - 297
  • [7] A Brief Survey on Forgetting from a Knowledge Representation and Reasoning Perspective
    Thomas Eiter
    Gabriele Kern-Isberner
    KI - Künstliche Intelligenz, 2019, 33 : 9 - 33
  • [8] A Brief Survey on Forgetting from a Knowledge Representation and Reasoning Perspective
    Eiter, Thomas
    Kern-Isberner, Gabriele
    KUNSTLICHE INTELLIGENZ, 2019, 33 (01): : 9 - 33
  • [9] Representing and Reasoning about Game Strategies
    Jiang, Guifei
    Zhang, Dongmo
    Perrussel, Laurent
    Zhang, Yan
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15), 2015, : 1975 - 1976
  • [10] Modeling as Scientific Reasoning-The Role of Abductive Reasoning for Modeling Competence
    zu Belzen, Annette Upmeier
    Engelschalt, Paul
    Krueger, Dirk
    EDUCATION SCIENCES, 2021, 11 (09):