Missing Link Prediction in Art Knowledge Graph using Representation Learning
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Abstract
Knowledge graphs are an important evolving field in Artificial Intelligence domain which has multiple applications such as in question answering, important information retrieval, information recommendation, Natural language processing etc. Knowledge graph has one big limitation i.e. Incompleteness, it is due to because of real world data are dynamic and continues evolving. This incompleteness of Knowledge graph can be overcome or minimized by using representation learning models. There are several models which are classified on the base of translation distance, semantic information and NN (Neural Network) based. Earlier the various embedding models are test on mostly two well-known datasets WN18RR & FB15k-237. In this paper, new dataset i.e. ArtGraph has been utilised for link prediction using representation learning models to enhance the utilization of ArtGraph in various domains. Experimental results shown ConvKB performed better over the other models for link prediction task.
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Nguyen, Dai Quoc, et al. "A novel embedding model for knowledge base completion based on convolutional neural network." arXiv preprint arXiv:1712.02121 (2017). https://doi.org/10.18653/v1/N18-2053
Dettmers, Tim, et al. "Convolutional 2d knowledge graph embeddings." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018. https://doi.org/10.1609/aaai.v32i1.11573
Castellano, Giovanna, Giovanni Sansaro, and Gennaro Vessio. "Integrating contextual knowledge to visual features for fine art classification." arXiv preprint arXiv:2105.15028 (2021).
Wang, Meihong, Linling Qiu, and Xiaoli Wang. "A survey on knowledge graph embeddings for link prediction." Symmetry 13.3 (2021): 485. https://doi.org/10.3390/sym13030485
Bordes, Antoine, et al. "Translating embeddings for modeling multi-relational data." Advances in neural information processing systems 26 (2013).
Wang, R.; Wang, M.; Liu, J.; Chen, W.; Cochez, M.; Decker, S. Leveraging Knowledge Graph Embeddings for Natural Language Question Answering. In Proceedings of the DASFAA 2019, Chiang Mai, Thailand, 22–25 April 2019; pp. 659–675. https://doi.org/10.1007/978-3-030-18576-3_39
Lehmann, J.; Isele, R.; Jakob, M.; Jentzsch, A.; Kontokostas, D. Mendes, P. N.; Hellmann, S.; Morsey, M.; Kleef, P. V.; Auer, S.; et al. DBpedia—A Large-Scale, Multilingual Knowledge base Extracted from Wikipedia; Semantic Web, Springer, 2015; Volume 6, pp. 167–195. https://doi.org/10.3233/SW-140134
Mahdisoltani, Farzaneh, Joanna Biega, and Fabian Suchanek. "Yago3: A knowledge base from multilingual wikipedias." 7th biennial conference on innovative data systems research. CIDR Conference, 2014.
Yang, Bishan, et al. "Embedding entities and relations for learning and inference in knowledge bases." arXiv preprint arXiv:1412.6575 (2014).
Lin, Y.; Liu, Z.; Sun, M.; Liu, Y.; Zhu, X. Learning Entity and Relation Embeddings for Knowledge Graph Completion; AAAI Press: Palo Alto, CA, USA, 2015; pp. 2181–2187. https://doi.org/10.1609/aaai.v29i1.9491
Trouillon, T.; Welbl, J.; Riedel, S.; Gaussier, É.; Bouchard, G. Complex Embeddings for Simple Link Prediction; ICML: New York City,NY, USA, 2016; pp. 2071–2080.
Wang, Zhen, et al. "Knowledge graph embedding by translating on hyperplanes." Proceedings of the AAAI conference on artificial intelligence. Vol. 28. No. 1. 2014. https://doi.org/10.1609/aaai.v28i1.8870
Fan, Miao, et al. "Transition-based knowledge graph embedding with relational mapping properties." Proceedings of the 28th Pacific Asia conference on language, information and computing. 2014.
Kanaparthi, V. (2022). Examining Natural Language Processing Techniques in the Education and Healthcare Fields. In International Journal of Engineering and Advanced Technology (Vol. 12, Issue 2, pp. 8–18). https://doi.org/10.35940/ijeat.b3861.1212222
Arya, V., Khan, R., & Aggarwal, Prof. M. (2022). A Chatbot Application by using Natural Language Processing and Artificial Intelligence Markup Language. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 3, pp. 1–7). https://doi.org/10.35940/ijsce.c3566.0712322
J, S., & Swamy, S. (2020). Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 3, pp. 1479–1485). https://doi.org/10.35940/ijitee.c8185.019320
Reddy, D. V., Padmaja, Dr. M., Kumar, K. M., Kiran, K. S., & Pramod, P. (2024). Chatbot Based Online Shopping Web Application. In Indian Journal of Data Communication and Networking (Vol. 3, Issue 4, pp. 7–14). https://doi.org/10.54105/ijdcn.b9782.03040623
Sharma, Dr. K., Garg, N., Pandey, A., Yadav, D., & Nikhil. (2021). Plagiarism Detection Technique using www and Wordnet. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 3, pp. 1–6). https://doi.org/10.54105/ijainn.b1015.061321