资源分类
》 资源 》论文》Recent Advances in Querying Probabilistic Knowledge Bases

Recent Advances in Querying Probabilistic Knowledge Bases

2019-11-06 | |  57 |   42 |   0
Abstract We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data. We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.

上一篇：Time Series Chains: A Novel Tool for Time Series Data Mining

下一篇：Five Years of Argument Mining: a Data-driven Analysis Elena Cabrio and Serena Villata

用户评价
全部评价

热门资源

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...