Ms. Guangyue Wu Profile

Guangyue Wu

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Proceedings Article | 11 October 2023 Paper

Research on multi-hop question answering algorithm based on knowledge graph

Jiefeng Zhao, Guangyue Wu, Yulong Tang, Yali Qi

Proceedings Volume 12800, 128004X (2023) https://doi.org/10.1117/12.3003968

KEYWORDS: Data modeling, Evolutionary algorithms, Semantics, Visualization, Education and training, Performance modeling, Detection and tracking algorithms, Vector spaces, Machine learning

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With the continuous increase of Internet information, the traditional single-hop query is not enough to meet the needs of users. For complex problems, multi-hop KGQA requires reasoning at multiple edges of the KG to arrive at the correct answer. KGS often lack many links, which poses additional challenges for KGQAs especially multi-jump KGQAs. In this paper, the main multi-hop question answering algorithms are divided into two categories: embedded-based multi-hop knowledge question answering reasoning and linked multi-hop knowledge question answering reasoning. The results show that the EmbedKGQA model performs better in prediction reasoning under the knowledge map with missing links by analyzing and comparing the performance of the subgraph matching and embedding prediction models on the Meta and WebQestionSP data sets. Finally, in view of the absence of knowledge graph data in practical application, we propose the development prospect of knowledge graph multi-hop algorithm from the two directions of combining pre-training with knowledge graph and using multi-modal model to expand the data in knowledge graph from multiple dimensions of the same entity.

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