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Research on Salient Reasoning for Commonsense Knowledge

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CCKS 2022 - Evaluation Track (CCKS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1711))

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Abstract

Salient reasoning of commonsense knowledge can help search engines better understand users’ search intentions and improve users’ search experience with intelligent product recommendation. Existing reasoning methods only use triple classification to determine the rationality of different entities in the knowledge graph, ignoring the significant changes between entities due to different search scenarios, and thus cannot infer the user’s true search intent. In order to solve the above problems, this paper tries various methods to improve the performance of the salient commonsense reasoning model, and proposes a multi-task learning model based on entity type discrimination and entity commonsense saliency reasoning, and at the same time proposes a Prompt-based commonsense saliency inference model. The model proposed won the first place in both the preliminary and semi-finals in the commodity commonsense knowledge saliency reasoning track of the 2022 China Conference on Knowledge Graph and Semantic Computing.

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Correspondence to Mingxu Ma .

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Ma, M., Wu, G., Yang, J. (2022). Research on Salient Reasoning for Commonsense Knowledge. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_22

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  • DOI: https://doi.org/10.1007/978-981-19-8300-9_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8299-6

  • Online ISBN: 978-981-19-8300-9

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