Abstract
Heterogeneous information networks are widely used in big data applications. These networks consist of multi-type information objects and relations. The appearance of the network can be changed depending on what perspective is used for modeling. Modeling relations between information objects has attracted recent attention. Although many related works have been proposed, they have limitations: they are hard to apply to unstructured data and they require continuous learning; and the results are often sparse. In this paper, we propose a new method based on a word-embedding technique that deduces various relations between information objects. We create viewpoint data that reflects any perspective on information objects and word embedding carried out by using these data. Using the proposed method, the system quantifies the relations between the information objects in heterogeneous information networks. The experiments use real world data to demonstrate the effectiveness of our methodology.
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This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future Planning (NRF - 2015R1A2 A2A01005304).
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Seo, J., Choi, S., Kim, Y.A. et al. Word embedding-based relation modeling in a heterogeneous information network. Multimed Tools Appl 77, 18529–18543 (2018). https://doi.org/10.1007/s11042-017-5008-z
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DOI: https://doi.org/10.1007/s11042-017-5008-z