Abstract
Entity resolution (ER), as the process of identifying records which depict the same real-world entity, plays a fundamental role in data integration and data cleaning tasks. Although deep learning techniques of data science have transformed various applications, there are few efforts to leverage these techniques to deal with entity resolution. We also observe the importance of overlapped tokens and the semantic similarity from pre-trained word vectors can benefit ER. To this end, we propose a deep learning based framework for ER, which can leverage the state-of-the-art techniques in deep neural network communities. We also propose an importance-and-semantics-aware approach for ER using a multilayer perceptron (MLP), to combine the importance of overlapped tokens, semantic similarity and textual similarity of corresponding attribute values of pairs. Comparative experiments demonstrate that our method outperforms the traditional method.
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Acknowledgments
This work was supported by the Ministry of Science and Technology of China, National Key Research and Development Program (Project Number: 2016YFB1000703), and the National Natural Science Foundation of China under Grant No. 61732014, No. 61332006, No. 61472321, No. 61502390 and No. 61672432.
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Xu, Y., Li, Z., Qi, W. (2018). An Importance-and-Semantics-Aware Approach for Entity Resolution Using MLP. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_8
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