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Deep Learning for Knowledge-Driven Ontology Stream Prediction

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Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding (CCKS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 957))

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Abstract

Time series prediction with data stream has been widely studied. Current deep learning methods e.g., Long Short-Term Memory (LSTM) perform well in learning feature representations from raw data. However, most of these models can narrowly learn semantic information behind the data. In this paper, we revisit LSTM from the perspective of Semantic Web, where streaming data are represented as ontology sequences. We propose a novel semantic-based neural network (STBNet) that (i) enriches the semantics of data stream with external text, and (ii) exploits the underlying semantics with background knowledge for time series prediction. Previous models mainly rely on numerical representation of values in raw data, while the proposed STBNet model creatively integrates semantic embedding into a hybrid neural network. We develop a new attention mechanism based on similarity among semantic embedding of ontology stream, and then we combine ontology stream and numerical analysis in the deep learning model. Furthermore, we also enrich ontology stream in STBNet, where Convolutional Neural Networks (CNNs) are incorporated in learning lexical representations of words in the text. The experiments show that STBNet outperforms state-of-the-art methods on stock price prediction.

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Notes

  1. 1.

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Correspondence to Huajun Chen .

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Deng, S., Pan, J.Z., Chen, J., Chen, H. (2019). Deep Learning for Knowledge-Driven Ontology Stream Prediction. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_5

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  • DOI: https://doi.org/10.1007/978-981-13-3146-6_5

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  • Online ISBN: 978-981-13-3146-6

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