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Domain Ontology-Driven Knowledge Graph Generation from Text
Authors: 
Zhixin Meng, Shaoxiong Zhan, Ruiqing Xu, Wolfgang Mayer, 
Ye Zhu, Hong-Yu Zhang, Chuan He, Keqing He, Debo Cheng, Zaiwen FengAuthors Info & Claims
Accepted on 09 November 2024
Abstract
A knowledge graph serves as a unified and standardized representation for extracting and representing textual information. In the field of knowledge extraction and representation research, named entity recognition and relation extraction provide effective solutions for knowledge graph generation tasks. However, it is a challenge that lies in extracting domain-specific knowledge from the rich and general textual corpora and generating corresponding domain knowledge graphs to support domain-specific reasoning, question-answering, and decision-making tasks. The hierarchical domain knowledge representation model (i.e. domain ontology) provides a solution for this problem. Therefore, we propose an end-to-end approach based on domain ontology embedding and pre-trained language models for domain knowledge graph generation from text, which incorporates domain node recognition and domain relation extraction phases. We evaluated our domain ontology-driven model on the Wikidata-TekGen dataset and the DBpedia-WebNLG dataset, and the results indicate that our approach based on the pre-trained language models with fewer parameters compared with the baseline models has significantly contributed to the domain knowledge graph generation without prompts.
References
[1]
Bilal Abu-Salih. 2021. Domain-specific knowledge graphs: A survey. Journal of Network and Computer Applications 185 (2021), 103076.
[2]
Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2020. Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training. arXiv preprint arXiv:2010.12688 (2020).
[3]
Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, and Ian Horrocks. 2021. OWL2Vec*: Embedding of OWL ontologies. Machine Learning 110, 7 (2021), 1813–1845.
[4]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[5]
Pierre L Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos, and Payel Das. 2020. Dualtkb: A dual learning bridge between text and knowledge base. arXiv preprint arXiv:2010.14660 (2020).
[6]
Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015).
[7]
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. 2021. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems 33, 2 (2021), 494–514.
[8]
Danh Le-Phuoc, Hoan Nguyen Mau Quoc, Hung Ngo Quoc, Tuan Tran Nhat, and Manfred Hauswirth. 2016. The graph of things: A step towards the live knowledge graph of connected things. Journal of Web Semantics 37 (2016), 25–35.
[9]
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019).
[10]
Linfeng Li, Peng Wang, Jun Yan, Yao Wang, Simin Li, Jinpeng Jiang, Zhe Sun, Buzhou Tang, Tsung-Hui Chang, Shenghui Wang, et al. 2020. Real-world data medical knowledge graph: construction and applications. Artificial intelligence in medicine 103 (2020), 101817.
[11]
Wei-Lin Chiang Lianmin Zheng, Ying Sheng and Lisa Dunlap. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. https://lmsys.org/blog/2023-03-30-vicuna/. [Accessed 11-07-2024].
[12]
Jinjiao Lin, Yanze Zhao, Weiyuan Huang, Chunfang Liu, and Haitao Pu. 2021. Domain knowledge graph-based research progress of knowledge representation. Neural Computing and Applications 33 (2021), 681–690.
[13]
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision. 2980–2988.
[14]
Zhihuang Lin, Dan Yang, and Xiaochun Yin. 2020. Patient similarity via joint embeddings of medical knowledge graph and medical entity descriptions. IEEE Access 8 (2020), 156663–156676.
[15]
Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, and Huajun Chen. 2021. MLBiNet: A cross-sentence collective event detection network. arXiv preprint arXiv:2105.09458 (2021).
[16]
Igor Melnyk, Pierre Dognin, and Payel Das. 2022. Knowledge graph generation from text. arXiv preprint arXiv:2211.10511 (2022).
[17]
Nandana Mihindukulasooriya, Sanju Tiwari, Carlos F Enguix, and Kusum Lata. 2023. Text2kgbench: A benchmark for ontology-driven knowledge graph generation from text. In International Semantic Web Conference. Springer, 247–265.
[18]
Fabian Neuhaus. 2018. What is an Ontology? arXiv preprint arXiv:1810.09171 (2018).
[19]
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne. 2023. Knowledge graphs: Opportunities and challenges. Artificial Intelligence Review (2023), 1–32.
[20]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485–5551.
[21]
Claire Gardent Thiago Castro Ferreira, Chris van der Lee Nikolai Ilinykh, Diego Moussallem Simon Mille, and Anastasia Shimorina. 2020. GitHub - WebNLG/WebNLG-Text-to-triples: WebNLG+ Challenge 2020: the automatic evaluation script for the text-to-RDF task — github.com. https://github.com/WebNLG/WebNLG-Text-to-triples. [Accessed 02-03-2024].
[22]
Chenguang Wang, Xiao Liu, and Dawn Song. 2020. Language models are open knowledge graphs. arXiv preprint arXiv:2010.11967 (2020).
[23]
Cheng Xie, Beibei Yu, Zuoying Zeng, Yun Yang, and Qing Liu. 2020. Multilayer internet-of-things middleware based on knowledge graph. IEEE Internet of Things Journal 8, 4 (2020), 2635–2648.
[24]
Hongbin Ye, Ningyu Zhang, Hui Chen, and Huajun Chen. 2022. Generative knowledge graph construction: A review. arXiv preprint arXiv:2210.12714 (2022).
[25]
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018. Graphrnn: Generating realistic graphs with deep auto-regressive models. In International conference on machine learning. PMLR, 5708–5717.
[26]
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2024. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36 (2024).
[27]
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr. 2015. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE international conference on computer vision. 1529–1537.
[28]
Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, and Xindong Wu. 2023. A comprehensive survey on automatic knowledge graph construction. arXiv preprint arXiv:2302.05019 (2023).
[29]
Dongzhuoran Zhou, Baifan Zhou, et al. 2022. Ontology reshaping for knowledge graph construction: Applied on bosch welding case. In International Semantic Web Conference. Springer, 770–790.
[30]
Dongzhuoran Zhou, Baifan Zhou, Zhuoxun Zheng, Ahmet Soylu, Ognjen Savkovic, Egor V Kostylev, and Evgeny Kharlamov. 2022. Schere: Schema reshaping for enhancing knowledge graph construction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 5074–5078.
[31]
Guangtong Zhou, Selasi Kwashie, et al. 2023. FASTAGEDS: fast approximate graph entity dependency discovery. In International Conference on Web Information Systems Engineering. Springer, 451–465.
[32]
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision. 2223–2232.
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- Domain Ontology-Driven Knowledge Graph Generation from Text
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EISSN:2836-8924
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 18 December 2024
Accepted: 09 November 2024
Revised: 16 August 2024
Received: 09 March 2024
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