Improving Embedding Model for Triple Knowledge Graph Using Neighborliness Vector

Sae-rom Cho, Han-joon Kim

Abstract


The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.


Full Text:

PDF

References


Dong, X., Chawla, N. V., and Swami, A., “metapath2vec: Scalable Representation Learning for Heterogeneous Networks,” In Proc. of Int. Conference on Information and Knowledge Management, pp. 135- 144, 2017.

Fionda, V. and Pirro, G., “Triple2Vec: Learning Triple Embeddings fromKnowledge Graphs,” AAAI, 2020.

Gao, Z., Fu, G., and Ouyang, C., “edge2vec: Representation learning using edge semantics for biomedical knowledge discovery,” BMC Bioinformatics, 2019.

Grover, A. and Leskovec, J., “node2vec: Scalable Feature Learning for Networks,” In Proc. of Int. Conference on Knowledge Discovery and Data Mining, pp. 855-864, 2016.

Hastie, T., Rosset, S., Zhu, J., and Zou, H., “Multi-class AdaBoost,” Statistics and Its Interface, Vol. 2, No. 3, pp. 349-360, 2009.

Hearst, M. A., “Support Vector Machines,” IEEE Intelligent Systems, Vol. 13, pp. 18- 28, 1998.

Hwang, S. H. and Kim, D. H., “BERT- based Classification Model for Korean Documents,” The Journal of Society for e-Business Studies, Vol. 25, No. 1, 2020.

Kipf, T. and Welling, M., “Semi-Supervised Classification with Graph Convolutional Networks,” Proceedings of the 5th International Conference on Learning Representation, 2017.

Krizhevsky, A., Sutskever, I., and Hinton, G. E., “ImageNet Classification with Deep Convolutional Neural Networks,” NIPs, 2012.

Lee, S.-E. and Kim, H.-J., “A New Ensemble Machine Learning Technique with Multiple Stacking,” The Jounal of Society for e-Business Studies, Vol. 25, No. 3, pp. 1-13, 2020.

Liaw, A. and Wiener, M., “Classification and Regression by randomForest,” R News, Vol. 2/3, pp. 18-22, 2002.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J., “Distributed Representations of Words and Phrases and their Compositionality,” NIPs, 2013.

Patil, T. R. and Sherekar, S. S., “Performance analysis of Naive Bayes and J48 classification algorithm for data classification,” International Journal of Computer Science and Applications, Vol. 6, No. 2, pp. 256-261, 2013.

Perozzi, B., Al-Rfou, R., and Skiena, S., “Deepwalk: OnLine Learning of Social rRpresentations,” In Proc. of KDD, pp. 701-710, 2014.

Pirro, G., “Building relatedness explanations from knowledge graphs,” ICAR- CNR, 2019.

Pregibon, D., “Logistic Regression Diagnostics,” The Annals of Statistics, Vol. 9, No. 4, pp. 705-724, 1981.

Scarsell, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G., “The Graph Neural Network Model,” IEEE Transactions on Neural Networks, Vol. 20, No. 1, pp. 61-80, 2009.

Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M., “Modeling Relational Data with Graph Convolutional Networks,” ESWC, pp. 593- 607, 2018.

Song, Y.-Y. and Ying, L. U., “Decision tree methods: applications for classification and prediction,” Shanghai Arch Psychiatry, Vol. 27, No. 2, pp. 130-135, 2015.

Zhang, M.-L. and Zhou, Z.-H., “ML-KNN: A lazy learning approach to multi-label learning,” Pattern Recognition, Vol. 40, No. 7, pp. 2038-2048, 2007.


Refbacks

  • There are currently no refbacks.