A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm

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摘要

There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive manufacturing relationships, which can be potentially used as decision support information for cross-enterprise manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract manufacturing relationships among various named entities (e.g., enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its’ web-based implementation in social manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises.

论文关键词:Manufacturing relationship extraction,Deep learning,Stacked denoising auto-encoder,Social manufacturing,Interaction context

论文评审过程:Received 12 September 2015, Revised 7 March 2016, Accepted 8 March 2016, Available online 14 March 2016, Version of Record 2 April 2016.

论文官网地址:https://doi.org/10.1016/j.knosys.2016.03.008