Abstract
In this paper, we demonstrate MDSE, which provides effective information extraction and searching for multi-source heterogeneous materials data that are collected as XML documents. The major features of MDSE are: (1) We propose a transfer-learning-based approach to extract material information from non-textual material data, including images, videos, etc. (2) We present a heterogeneous-graph-based method to extract the semantic relationships among material data. (3) We build a search engine with both Google-like and tabular searching UIs to provide functional searching on integrated material data. After a brief introduction to the architecture and key technologies of MDSE, we present a case study to demonstrate the working process and the effectiveness of MDSE.
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Acknowledgements
This study is supported by the National Key Research and Development Program of China (2018YFB0704404) and the National Science Foundation of China (61672479).
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Liang, J., Jin, P., Mu, L., Hong, X., Qi, L., Wan, S. (2020). MDSE: Searching Multi-source Heterogeneous Material Data via Semantic Information Extraction. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_47
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DOI: https://doi.org/10.1007/978-3-030-59419-0_47
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