Context-Aware Presentation of Linked Data on Mobile

被引:1
作者
Costabello, Luca [1 ]
Gandon, Fabien [2 ]
机构
[1] INRIA, French Inst Res Comp Sci, Sophia Antipolis, France
[2] INRIA, Informat & Comp Sci, Sophia Antipolis, France
关键词
Adaptive Interfaces; Context Awareness; Context Matching; Error-Tolerant Subgraph Isomorphism; Linked Data; Linked Data Visualization; Ubiquitous Semantic Web; ALGORITHM;
D O I
10.4018/ijswis.2014100103
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.
引用
收藏
页码:45 / 76
页数:32
相关论文
共 29 条
  • [1] [Anonymous], 2007, Ontology matching, DOI 10.1007/978-3-540-49612-0
  • [2] [Anonymous], 14 INT WORLD WID WEB
  • [3] [Anonymous], 2003, IIWeb
  • [4] [Anonymous], SEM WEB WORKSH
  • [5] Auer S, 2010, LECT NOTES COMPUT SC, V6089, P211, DOI 10.1007/978-3-642-13489-0_15
  • [6] Bolchini C, 2007, SIGMOD REC, V36, P19, DOI 10.1145/1361348.1361353
  • [7] Carroll JJ, 2002, LECT NOTES COMPUT SC, V2342, P5
  • [8] Castano S, 2011, LECT NOTES ARTIF INT, V6050, P167, DOI 10.1007/978-3-642-20795-2_7
  • [9] Champin P.-A., 2009, SCR SEM WEB WORKSH E
  • [10] Thirty years of graph matching in pattern recognition
    Conte, D
    Foggia, P
    Sansone, C
    Vento, M
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2004, 18 (03) : 265 - 298