Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
Table 3: Evaluation results on triple classification.
['[BOLD] Method', 'FB15K']
4.3.2 Results From the results, we observe that: (1) Our attention-based models significantly outperform all baselines, which indicates the capability of our methods in modeling knowledge representations. (2) STKRL (RNN w/o ATT) outperforms TransE, which implies the significance of textual information. Moreover, the attention-based STKRL models outperform Wang’s method which also encodes textual information into knowledge representations. It confirms that our sentence encoders can better understand the meaning of sentences, and thus get better performances. (3) Attention is the key component in our model. It can automatically select the more informative reference sentences to represent an entity, alleviating the noises caused by low-quality reference sentences. (4) STKRL (LSTM+ATT) achieves the best performance, successfully capturing the rich information in long-distance dependencies. Besides, STKRL (RNN+P+ATT) outperforms STKRL (RNN+ATT), which also demonstrates the advantages of pooling strategy.