Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
This work addresses knowledge representation learning for AI systems by integrating textual information, but it is incremental as it builds on existing methods with improvements in sentence selection and encoding.
The paper tackled the problem of constructing knowledge representations from plain texts by addressing challenges in utilizing sequential contexts and selecting informative sentences, resulting in a method that outperformed baselines on triple classification and link prediction tasks.
Textual information is considered as significant supplement to knowledge representation learning (KRL). There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically select those informative sentences of the corresponding entities for KRL. In this paper, we propose the Sequential Text-embodied Knowledge Representation Learning to build knowledge representations from multiple sentences. Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the entity. Then we further design an attention model to measure the informativeness of each sentence, and build text-based representations of entities. We evaluate our method on two tasks, including triple classification and link prediction. Experimental results demonstrate that our method outperforms other baselines on both tasks, which indicates that our method is capable of selecting informative sentences and encoding the textual information well into knowledge representations.