DL-Lite Ontology Revision Based on An Alternative Semantic Characterization

被引:6
|
作者
Wang, Zhe [1 ]
Wang, Kewen [1 ]
Topor, Rodney [1 ]
机构
[1] Griffith Univ, Sch Informat & Commun Technol, Brisbane, Qld 4111, Australia
基金
澳大利亚研究理事会;
关键词
Theory; Description logics; DL-Lite; ontology change; revision; INSTANCE-LEVEL; LOGIC;
D O I
10.1145/2786759
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Ontology engineering and maintenance require (semi-) automated ontology change operations. Intensive research has been conducted on TBox and ABox changes in description logics (DLs), and various change operators have been proposed in the literature. Existing operators largely fall into two categories: syntax-based and model-based. While each approach has its advantages and disadvantages, an important topic that has rarely been explored is how to achieve a balance between syntax-based and model-based approaches. Also, most existing operators are specially designed for either TBox change or ABox change, and cannot handle the general ontology revision task-given a DL knowledge base (KB, a pair consisting of a TBox and an ABox), how to revise it by a set of TBox and ABox axioms (i.e., a new DL KB). In this article, we introduce an alternative structure for DL-Lite, called a featured interpretation, and show that featured models provide a finite and tight characterization to the classical semantics of DL-Lite. A key issue for defining a change operator is the so-called expressibility, that is, whether a set of models (or featured models here) is axiomatizable in DLs. It is indeed much easier to obtain expressibility results for featured models than for classical DL models. As a result, the new semantics determined by featured models provides a method for defining and studying various changes of DL-Lite KBs that involve both TBoxes and ABoxes. To demonstrate the usefulness of the new semantic characterization in ontology change, we define two revision operators for DL-Lite KBs using featured models and study their properties. In particular, we show that our two operators both satisfy AGM postulates. We show that the complexity of our revisions is Pi(P)(2)-complete, that is, on the same level as major revision operators in propositional logic, which further justifies the feasibility of our revision approach for DL-Lite. Also, we develop algorithms for these DL-Lite revisions.
引用
收藏
页数:37
相关论文
共 50 条
  • [1] DL-Lite Contraction and Revision
    Zhuang, Zhiqiang
    Wang, Zhe
    Wang, Kewen
    Qi, Guilin
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2016, 56 : 329 - 378
  • [2] On the revision of prioritized DL-lite knowledge bases
    1600, Springer Verlag (8720):
  • [3] A Graph-Based Approach to Ontology Debugging in DL-Lite
    Fu, Xuefeng
    Zhang, Yong
    Qi, Guilin
    SEMANTIC TECHNOLOGY (JIST 2014), 2015, 8943 : 33 - 46
  • [4] Contraction and Revision over DL-Lite TBoxes
    Zhuang, Zhiqiang
    Wang, Zhe
    Wang, Kewen
    Qi, Guilin
    PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2014, : 1149 - 1155
  • [5] Instance-Driven Ontology Evolution in DL-Lite
    Wang, Zhe
    Wang, Kewen
    Zhuang, Zhiqiang
    Qi, Guilin
    PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2015, : 1656 - 1662
  • [6] A Prioritized Assertional-Based Revision for DL-Lite Knowledge Bases
    Benferhat, Salem
    Bouraoui, Zied
    Papini, Odile
    Wuerbel, Eric
    LOGICS IN ARTIFICIAL INTELLIGENCE, JELIA 2014, 2014, 8761 : 442 - 456
  • [7] Capturing Instance Level Ontology Evolution for DL-Lite
    Kharlamov, Evgeny
    Zheleznyakov, Dmitriy
    SEMANTIC WEB - ISWC 2011, PT I, 2011, 7031 : 321 - 337
  • [8] A New Approach to Knowledge Base Revision in DL-Lite
    Wang, Zhe
    Wang, Kewen
    Topor, Rodney
    PROCEEDINGS OF THE TWENTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-10), 2010, : 369 - 374
  • [9] A graph-based method for interactive mapping revision in DL-Lite
    Li, Weizhuo
    Ji, Qiu
    Zhang, Songmao
    Fu, Xuefeng
    Qi, Guilin
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 211