Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
This addresses the challenge of interpretable text-to-entity mapping for knowledge graph applications, though it appears incremental as it builds on existing sequence-to-sequence approaches.
The paper tackles the problem of mapping unrestricted text to knowledge graph entities by framing it as a sequence-to-sequence task, where the decoder predicts hierarchical paths in the graph, achieving results comparable to state-of-the-art systems.
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.