We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the target node following hypernym-hyponym relationships. In this way, and in contrast to other text-to-entity mapping systems, our model outputs hierarchically structured predictions that are fully interpretable in the context of the underlying ontology, in an end-to-end manner. We present a proof-of-concept experiment with encouraging results, comparable to those of state-of-the-art systems.
翻译:我们提出了一种新的方法,通过将任务设计成一个顺序到顺序问题,将不受限制的文字绘制给知识图形实体。 具体地说,考虑到输入文字的编码状态,我们的解码器直接预测知识图形中的路径,从根开始,到超nym-hyponyym关系的目标节点结束。 这样,与其他文本到实体的绘图系统相比,我们的模型输出按等级排列的预测以端到端的方式在基本本体学方面完全可以解释的。我们提出了一个概念的证明实验,与最先进的系统相比,可以产生令人鼓舞的结果。