Human-computer Interaction Model Based on Knowledge Graph Ripple Network

HUANG Hongcheng; LIAO Qiang; HU Min; TAO Yang; KOU Lan

doi:10.11999/JEIT200817

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 221-229

HUANG Hongcheng, LIAO Qiang, HU Min, TAO Yang, KOU Lan. Human-computer Interaction Model Based on Knowledge Graph Ripple Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 221-229. doi: 10.11999/JEIT200817

Citation:

HUANG Hongcheng, LIAO Qiang, HU Min, TAO Yang, KOU Lan. Human-computer Interaction Model Based on Knowledge Graph Ripple Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 221-229. doi: 10.11999/JEIT200817

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Human-computer Interaction Model Based on Knowledge Graph Ripple Network

doi: 10.11999/JEIT200817

HUANG Hongcheng^{1, 2},
LIAO Qiang¹,
HU Min¹,
TAO Yang^{1, 2
,
,},
KOU Lan¹

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Engineering Research Center of Communication Software, Chongqing 400065, China

Funds: The National Key Research and Development Project (2019YFB2102001), The National Natural Science Foundation of China (61871062)

Received Date: 2020-09-18
Rev Recd Date: 2021-03-19

Available Online: 2021-06-21

Publish Date: 2022-01-10

Abstract

Abstract

To solve the problems of lack of background knowledge and poor consistency of robot response in the existing human-computer interaction, a human-computer interaction model based on the ripple network of knowledge graph is proposed. In order to achieve a more natural and intelligent human-computer interaction system, this model simulates the real human-human interaction process. Firstly, the human-computer interaction affective friendliness is obtained by calculating the human-computer interaction emotional evaluation value and the human-computer interaction emotional certainty degree. Then, the external knowledge graph is introduced as the background knowledge of robots, and the dialogue entity is embedded into the ripple network of knowledge graph to obtain the potential interested entity content of the participants. Finally, considering the emotional friendliness and content friendliness, the robot response is given. The experimental results show that, compared with other models, robots that have background knowledge and consider emotional friendliness improve emotionality and coherence when interacting with human.
- Human-computer interaction,
- Background knowledge,
- Affective friendliness,
- Knowledge graph ripple network

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References(20)

References

[1]	RICHMOND J and NELSON C A. Relational memory during infancy: Evidence from eye tracking[J]. Developmental Science, 2009, 12(4): 549–556. doi: 10.1111/j.1467-7687.2009.00795.x
[2]	黄宏程, 刘宁, 胡敏, 等. 基于博弈的机器人认知情感交互模型[J]. 电子与信息学报, 2019, 41(10): 2471–2478. doi: 10.11999/JEIT180867 HUANG Hongcheng, LIU Ning, HU Min, et al. Cognitive emotion interaction model of robot based on game theory[J]. Journal of Electronics &Information Technology, 2019, 41(10): 2471–2478. doi: 10.11999/JEIT180867
[3]	LIU Xin, XIE Lun, and WANG Zhiliang. Empathizing with emotional robot based on cognition reappraisal[J]. China Communications, 2017, 14(9): 100–113. doi: 10.1109/CC.2017.8068769
[4]	RODRÍGUEZ L F, GUTIERREZ-GARCIA J O, and RAMOS F. Modeling the interaction of emotion and cognition in Autonomous Agents[J]. Biologically Inspired Cognitive Architectures, 2016, 17: 57–70. doi: 10.1016/j.bica.2016.07.008
[5]	NANTY A and GELIN R. Fuzzy controlled PAD emotional state of a NAO robot[C]. 2013 Conference on Technologies and Applications of Artificial Intelligence, Taipei, China, 2013: 90–96. doi: 10.1109/TAAI.2013.30.
[6]	LOWE R, POW N, CHARLIN L, et al. Incorporating unstructured textual knowledge sources into neural dialogue systems[C]. Machine Learning for Spoken Language Understanding and Interaction, Montreal, Canada, 2015.
[7]	YOUNG T, CAMBRIA E, CHATURVEDI I, et al. Augmenting end-to-end dialog systems with commonsense knowledge[C]. The 32nd AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, USA, 2018: 4970–4977.
[8]	YANG Liu, QIU Minghui, QU Chen, et al. Response ranking with deep matching networks and external knowledge in information-seeking conversation systems[C]. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, New York, USA, 2018: 245–254. doi: 10.1145/3209978.3210011.
[9]	LIN Yankai, LIU Zhiyuan, SUN Maosong, et al. Learning entity and relation embeddings for knowledge graph completion[C]. The 29th AAAI Conference on Artificial Intelligence, Austin, USA, 2015: 2181–2187.
[10]	于洪涛, 丁悦航, 刘树新, 等. 一种基于超节点理论的本体关系消冗算法[J]. 电子与信息学报, 2019, 41(7): 1633–1640. doi: 10.11999/JEIT180793 YU Hongtao, DING Yuehang, LIU Shuxin, et al. Eliminating structural redundancy based on super-node theory[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1633–1640. doi: 10.11999/JEIT180793
[11]	丁永刚, 李石君, 付星, 等. 面向时序感知的多类别商品方面情感分析推荐模型[J]. 电子与信息学报, 2018, 40(6): 1453–1460. doi: 10.11999/JEIT170938 DING Yonggang, LI Shijun, FU Xing, et al. Temporal-aware multi-category products recommendation model based on aspect-level sentiment analysis[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1453–1460. doi: 10.11999/JEIT170938
[12]	PARK J W, KIM W H, LEE W H, et al. How to completely use the PAD space for socially interactive robots[C]. 2011 IEEE International Conference on Robotics and Biomimetics, Karon Beach, Thailand, 2011: 3005–3010. doi: 10.1109/ROBIO.2011.6181762.
[13]	SIL A and YATES A. Re-ranking for joint named-entity recognition and linking[C]. The 22nd ACM international conference on Information & Knowledge Management, San Francisco, USA, 2013: 2369–2374. doi: 10.1145/2505515.2505601.
[14]	MILNE D and WITTEN I H. Learning to link with wikipedia[C]. The 17th ACM conference on Information and Knowledge Management, Napa Valley, USA, 2008: 509–518. doi: 10.1145/1458082.1458150.
[15]	MIKOLOV T, SUTSKEVER I, CHEN Kai, et al. Distributed representations of words and phrases and their compositionality[C]. The 26th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2013: 3111–3119.
[16]	JI Guoliang, HE Shizhu, XU Liheng, et al. Knowledge graph embedding via dynamic mapping matrix[C]. The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, 2015: 687–696. doi: 10.3115/v1/P15-1067.
[17]	WANG Hongwei, ZHANG Fuzheng, WANG Jialin, et al. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems[C]. The 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 2018: 417–426. doi: 10.1145/3269206.3271739.
[18]	SUTSKEVER I, VINYALS O, and LE Q V. Sequence to sequence learning with neural networks[C]. The 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 3104–3112.
[19]	GUNTHER C. Tutorial-ChatterBot 1.0.2 documentation[EB/OL]. https://chatterbot.readthedocs.io/en/stable/tutorial.html, 2019.
[20]	ZHANG Rui, WANG Zhenyu, and MAI Dongcheng. Building emotional conversation systems using multi-task Seq2Seq learning[C]. The 6th CCF International Conference on Natural Language Processing and Chinese Computing, Dalian, China, 2018: 612–621. doi: 10.1007/978-3-319-73618-1_51.