MoMM - Fast interreduction and retrieval in large libraries of formalized mathematics

被引:33
|
作者
Urban, J [1 ]
机构
[1] Charles Univ Prague, Dept Theoret Comp Sci, Prague, Czech Republic
关键词
D O I
10.1142/S0218213006002588
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
MoMM (in the narrower sense) is a tool allowing fast interreduction of a high number of clauses, dumping and fast-loading of the interreduced. clause sets, and their use for real-time retrieval of matching clauses in an interactive mode. MoMM's main task is now providing these services for the world's largest body of formalized mathematics the Mizar Mathematical Library (MML), which uses a richer formalism than just pure predicate logic. This task leads to a number of features (strength, speed, memory efficiency, dealing with the richer Mizar logic, etc.) required from MoMM, and we describe the choices taken in its implementation corresponding to these requirements. An important part of MoMM (in the wider sense) are the tools exporting the richer logic of MML into the clause-like format suitable for fast interreduction, and the tools allowing the use of MoMM as an interactive advisor for the authors of Mizar articles. These tools and choices taken in their implementation are also described here. Next we present some results of the interreduction of MML, which provide an interesting information about subsumption and repetition in the MML and can be used for its refactoring. This interreduction reveals that more than 2 percent of the main MML theorems are subsumed by others, and that for more than 50 percent of the internal lemmas proved by Mizar authors MoMM can provide useful advice for their justification. Finally some problems and possible future work are discussed.
引用
收藏
页码:109 / 130
页数:22
相关论文
共 50 条
  • [31] Jointly sparse fast hashing with orthogonal learning for large-scale image retrieval
    Xu, Honghao
    Lai, Zhihui
    Kong, Heng
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 2023, 119
  • [32] A fast multi-resolution search algorithm for optimal retrieval in large multimedia databases
    Song, BC
    Kim, MJ
    Ra, JB
    STORAGE AND RETRIEVAL FOR MEDIA DATABASES 2000, 2000, 3972 : 215 - 221
  • [33] Fast decentralized learning of a Gaussian mixture model for large-scale multimedia retrieval
    Nikseresht, A
    Gelgon, M
    PROCEEDINGS OF THE 14TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING, 2005, : 373 - 379
  • [34] AUTOMATED MICROFICHE TERMINAL - FAST RETRIEVAL FROM VERY LARGE DATA-BASES
    DUERDEN, F
    GEC-JOURNAL OF SCIENCE & TECHNOLOGY, 1976, 43 (02): : 51 - 60
  • [35] Fast age-based Chinese Calligraphic Character Retrieval on Large Scale Data
    Gao Pengcheng
    Wu Jiangqin
    Lin Yuan
    Xia Yang
    Mao Tianjiao
    Wei Baogang
    2014 IEEE/ACM JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL), 2014, : 211 - 218
  • [36] Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval
    Wang, Xingzhi
    Liu, Xin
    Peng, Shujuan
    Cheung, Yiu-ming
    Hu, Zhikai
    Wang, Nannan
    2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), 2019, : 1348 - 1353
  • [37] Fast Discrete Collaborative Multi-Modal Hashing for Large-Scale Multimedia Retrieval
    Zheng, Chaoqun
    Zhu, Lei
    Lu, Xu
    Li, Jingjing
    Cheng, Zhiyong
    Zhang, Hanwang
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 32 (11) : 2171 - 2184
  • [38] FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
    Liu, Xin
    Wang, Xingzhi
    Yiu-ming Cheung
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (11) : 6306 - 6320
  • [39] A fast online spherical hashing method based on data sampling for large scale image retrieval
    Weng, Zhenyu
    Zhu, Yuesheng
    Lan, Yinhe
    Huang, Long-Kai
    NEUROCOMPUTING, 2019, 364 : 209 - 218
  • [40] Large-Scale Storage of Whole Slide Images and Fast Retrieval of Tiles Using DRAM
    Barron, Daniel E. Lopez
    Rao, Praveen
    Rao, Deepthi
    Tawfik, Ossama
    Zachariah, Arun
    BIG DATA II: LEARNING, ANALYTICS, AND APPLICATIONS, 2020, 11395