Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling

Zhang, Wenwen; Gao, Yanfang; Xu, Zifan; Wang, Lin; Ji, Shengxu; Zhang, Xiaohui; Yuan, Guanyu

doi:10.3390/app15020547

Open AccessArticle

Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling

by

Wenwen Zhang

¹,

Yanfang Gao

^1,*,

Zifan Xu

²,

Lin Wang

¹,

Shengxu Ji

¹,

Xiaohui Zhang

¹

and

Guanyu Yuan

¹

School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China

²

School of Science, Shandong Jianzhu University, Jinan 250101, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(2), 547; https://doi.org/10.3390/app15020547

Submission received: 7 December 2024 / Revised: 2 January 2025 / Accepted: 6 January 2025 / Published: 8 January 2025

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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Abstract

Intent detection and slot filling tasks share common semantic features and are interdependent. The abundance of professional terminology in specific domains, which poses difficulties for entity recognition, subsequently impacts the performance of intent detection. To address this issue, this paper proposes a co-interactive model based on a knowledge graph (CIMKG) for intent detection and slot filling. The CIMKG model comprises three key components: (1) a knowledge graph-based shared encoder module that injects domain-specific expertise to enhance its semantic representation and solve the problem of entity recognition difficulties caused by professional terminology and then encodes short utterances; (2) a co-interactive module that explicitly establishes the relationship between intent detection and slot filling to address the inter-dependency of these processes; (3) two decoders that decode the intent detection and slot filling. The proposed CIMKG model has been validated using question–answer corpora from both the medical and architectural safety fields. The experimental results demonstrate that the proposed CIMKG model outperforms benchmark models.

Keywords: co-interactive module; knowledge graph; intent detection; slot filling

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MDPI and ACS Style

Zhang, W.; Gao, Y.; Xu, Z.; Wang, L.; Ji, S.; Zhang, X.; Yuan, G. Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling. Appl. Sci. 2025, 15, 547. https://doi.org/10.3390/app15020547

AMA Style

Zhang W, Gao Y, Xu Z, Wang L, Ji S, Zhang X, Yuan G. Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling. Applied Sciences. 2025; 15(2):547. https://doi.org/10.3390/app15020547

Chicago/Turabian Style

Zhang, Wenwen, Yanfang Gao, Zifan Xu, Lin Wang, Shengxu Ji, Xiaohui Zhang, and Guanyu Yuan. 2025. "Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling" Applied Sciences 15, no. 2: 547. https://doi.org/10.3390/app15020547

APA Style

Zhang, W., Gao, Y., Xu, Z., Wang, L., Ji, S., Zhang, X., & Yuan, G. (2025). Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling. Applied Sciences, 15(2), 547. https://doi.org/10.3390/app15020547

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling

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