Abstract
Recently, machine learning has been widely used for services classification that plays a crucial role in services discovery, selection, and composition. The current methods mostly rely on only one data modality (e.g. services description) for web services classification but fail to fully exploit other readily available data modalities (e.g. services names, and URL). In this paper, a novel MultiModal-Attention-based deep neural network (MMA-Net) is proposed to facilitate the web services classification task via effective feature learning from multiple readily available data modalities. Specifically, a new multimodal feature learning module is introduced to achieve effective message passing and information exchanging among multiple modalities. We conduct experiments on the real-world web services dataset using various evaluation metrics, and the results show that our framework achieves the state-of-the-art results.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant No. 62006012 and No. 61732019, and the Opening Project of Shanghai Trusted Industrial Control Platform No. KH54327801.
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Zhang, J., Lei, C., Yang, Y., Wang, B., Chen, Y. (2021). MMA-Net: A MultiModal-Attention-Based Deep Neural Network for Web Services Classification. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_48
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DOI: https://doi.org/10.1007/978-3-030-91431-8_48
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