Databases Theory and Applications: 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3–7, 2020, Proceedings

Conceptual modelling plays an important role in information system design and is one of its key activities. The modelling process usually involves domain experts and knowledge engineers who work together to bring out the required knowledge for building the information system. The most popular modelling approaches to develop these models include entity relationship modelling, object role modelling, and object-oriented modelling. These conceptual models are usually constructed graphically but are often difficult to understand by domain experts. In this paper we show how a restricted natural language can be used for writing a precise and consistent specification that is automatically translated into a description logic representation from which a conceptual model can be derived. This conceptual model can be rendered graphically and then verbalised again in the same restricted natural language as the specification. This process can be achieved with the help of a bi-directorial grammar that allows for semantic round-tripping between the representations.

[1]  Yang Yang,et al.  Deep Semantic Indexing Using Convolutional Localization Network with Region-Based Visual Attention for Image Database , 2017, ADC.

[2]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[3]  Yao Hu,et al.  Iterative Multi-View Hashing for Cross Media Indexing , 2014, ACM Multimedia.

[4]  Guiguang Ding,et al.  Collective Matrix Factorization Hashing for Multimodal Data , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Yuxin Peng,et al.  Unsupervised Generative Adversarial Cross-modal Hashing , 2017, AAAI.

[6]  Ling Shao,et al.  Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval , 2018, IEEE Transactions on Image Processing.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[9]  Jakob Uszkoreit,et al.  A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.

[10]  Wu-Jun Li,et al.  Deep Cross-Modal Hashing , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Zi Huang,et al.  Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation , 2019, ACM Multimedia.

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Nikos Paragios,et al.  Data fusion through cross-modality metric learning using similarity-sensitive hashing , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Mark J. Huiskes,et al.  The MIR flickr retrieval evaluation , 2008, MIR '08.

[15]  Yi Zhen,et al.  Co-Regularized Hashing for Multimodal Data , 2012, NIPS.

[16]  Jieping Ye,et al.  A least squares formulation for canonical correlation analysis , 2008, ICML '08.

[17]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[18]  Xuelong Li,et al.  Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval , 2017, IEEE Transactions on Image Processing.

[19]  Jianmin Wang,et al.  Semantics-preserving hashing for cross-view retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Wenwu Zhu,et al.  Learning Compact Hash Codes for Multimodal Representations Using Orthogonal Deep Structure , 2015, IEEE Transactions on Multimedia.

[21]  Mirella Lapata,et al.  Long Short-Term Memory-Networks for Machine Reading , 2016, EMNLP.

[22]  Jonghyun Choi,et al.  Predictable Dual-View Hashing , 2013, ICML.

[23]  Cordelia Schmid,et al.  Areas of Attention for Image Captioning , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[24]  Jungong Han,et al.  Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval , 2018, IJCAI.

[25]  Yejin Choi,et al.  Collective Generation of Natural Image Descriptions , 2012, ACL.

[26]  Tao Mei,et al.  Boosting Image Captioning with Attributes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  Cyrus Rashtchian,et al.  Every Picture Tells a Story: Generating Sentences from Images , 2010, ECCV.

[28]  Philip S. Yu,et al.  Composite Correlation Quantization for Efficient Multimodal Retrieval , 2015, SIGIR.

[29]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Dongqing Zhang,et al.  Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization , 2014, AAAI.

[31]  Alexander M. Rush,et al.  Structured Attention Networks , 2017, ICLR.

[32]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.

[33]  Yuxin Peng,et al.  Multi-Scale Correlation for Sequential Cross-modal Hashing Learning , 2018, ACM Multimedia.

[34]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[35]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[36]  Heng Tao Shen,et al.  Attention-based LSTM with Semantic Consistency for Videos Captioning , 2016, ACM Multimedia.

[37]  Wei Liu,et al.  Discrete Graph Hashing , 2014, NIPS.

[38]  Zi Huang,et al.  Robust discrete code modeling for supervised hashing , 2018, Pattern Recognit..

[39]  Li Fei-Fei,et al.  DenseCap: Fully Convolutional Localization Networks for Dense Captioning , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2015, CVPR.

[41]  Raghavendra Udupa,et al.  Learning Hash Functions for Cross-View Similarity Search , 2011, IJCAI.

[42]  Guiguang Ding,et al.  Latent semantic sparse hashing for cross-modal similarity search , 2014, SIGIR.

[43]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[44]  Heng Tao Shen,et al.  Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning , 2017, IJCAI.

[45]  Peter Young,et al.  Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics , 2013, J. Artif. Intell. Res..