CKGSE: A Prototype Search Engine for Chinese Knowledge
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Wang, Xiaoxia; Lin, Tengteng; Luo, Weiqing; Cheng, Gong; Qu, Yuzhong

doi:10.1162/dint_a_00118

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…In addition, for Complex QA, in this paper, the external knowledge is limited to document corpora which mainly contain unstructured text. We hope to cover other structured knowledge sources such as knowledge bases (KBs) (Wang et al, 2022a) and tables in the future, since knowledge from different sources complement each other. This can be achieved by incorporating other QA modules during reasoning such as semantic parsers that can execute on KBs or tables.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions

Cao,

Zhang,

Shi

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought (CoT) reasoning. However, they tend to generate factually incorrect reasoning steps when the required knowledge is not available or up-to-date in models' parameters. Recent works turn to retrieving external knowledge to augment CoT reasoning. Despite being promising, these chain-based methods suffer from: 1) Negative retrieval. Unnecessary or incorrect retrieval may mislead the reasoning; 2) Limited sight. Lacking the ability to look backward or forward, a local error in one step will propagate along the chain.In this paper, we propose a novel approach: Probabilistic Tree-of-thought Reasoning (ProbTree). First, LLMs translate a complex question into a query tree, in which each non-root node denotes a sub-question of its parent node. Then, probabilistic reasoning is conducted over the tree, by solving questions from leaf to root considering the confidence of both question decomposing and answering. During reasoning, for leaf nodes, LLMs choose a more confident answer from Closed-book QA that employs parametric knowledge and Openbook QA that employs retrieved external knowledge, thus eliminating the negative retrieval problem. For non-leaf nodes, with the hierarchical structure, LLMs have broader sights and are able to globally reason with the information from child nodes, thus recovering from local errors. The experiments on three Complex QA datasets under the open-domain setting show that our approach outperforms SOTA methods significantly, demonstrating the effect of probabilistic tree-of-thought reasoning.

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Section: Discussionmentioning

confidence: 99%

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions

Cao,

Zhang,

Shi

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

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“…Therefore, it is no longer just a search engine based on string retrieval and matching, but gradually developed into an intelligent search platform that can truly understand the meaning of user input search strings, and can return the integrated facts as results more accurately. At the same time, Baidu also followed Google's technology and launched the Chinese Knowledge Graph [8,9]. Subsequently, a knowledge graph began to rapidly extend from search engines to finance [10], agriculture [11], medical [12,13] and other industries, and became an indispensable technical means for integrating and analyzing data in the era of big data.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

Li,

Guo,

et al. 2023

Applied Sciences

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Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.

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