Deep Learning for Knowledge-Driven Ontology Stream Prediction

Shumin Deng, Jeff Z. Pan, Jiaoyan Chen, Huajun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationKnowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding
EditorsJun Zhao, Frank van Harmelen, Jie Tang, Xianpei Han, Quan Wang, Xianyong Li
Place of PublicationSingapore
PublisherSpringer Singapore
Pages52-64
Number of pages13
ISBN (Electronic)978-981-13-3146-6
ISBN (Print)978-981-13-3145-9
DOIs
Publication statusPublished - 7 Dec 2018
Event3rd China Conference on Knowledge Graph and Semantic Computing - Tianjin, China
Duration: 14 Aug 202017 Aug 2020
http://www.ccks2018.com

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Singapore
Volume957
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd China Conference on Knowledge Graph and Semantic Computing
Abbreviated titleCCKS 2018
CountryChina
CityTianjin
Period14/08/2017/08/20
Internet address

Keywords

  • Ontology stream
  • Deep learning
  • Time series prediction

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