Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
Table 6: Evaluation results on link prediction.
['[BOLD] Metric', 'Mean', 'Rank', 'Hits@', '10(%)']
Our model significantly outperforms all baselines in both Mean Rank and Hits@10, which demonstrates the effectiveness and robustness of our model. (2) STKRL (RNN+P+ATT) and STKRL (LSTM+ATT) significantly outperform Wang ’s method. It is because that we utilize sentence encoders to model textual information with the help of word orders, instead of simply considering separate words used in Wang’s method. Moreover, the attention-based STKRL models could pay more attention to those informative reference sentences when constructing text-based representations. (3) STKRL (LSTM+ATT) achieves the best performances, which indicates the power in utilizing better sentence encoders.