Generating Knowledge Graph Paths from Textual Definitionsusing Sequence-to-Sequence Models
Table 2: Ancestor F1 results. Numbers in bold represent the best performing system on a graph. Models marked with ∗ make use of pre-trained word embedding in their encoder. Lambda (λ) is defined in Section 3.1. We use the same number of epochs, batch size and number of latent dimensions both for MS-LSTM and our models (Appendix C).
['Models', 'PATO', 'WNanimal.n.01', 'WNplant.n.02', 'HDO', 'HPO', 'GO', 'WNentity.n.01']
MS-LSTM and our models that are above the last three rows use randomly initialised word vectors. We had four observations: (1) without pre-trained word embeddings in the encoder our model outperforms the best MS-LSTMλ=0.5 only on two of the seven graphs, (2) the text2edges∗ model outperforms all the other models including MS-LSTM∗λ=0.5, (3) the text2edges model can better exploit pre-trained word embeddings than MS-LSTM, (4) our model performs better when the paths are represented using edges (rather than nodes). the Ancestor F1 score decreases.