# Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models

## TL;DR

This paper introduces a sequence-to-sequence model that maps textual definitions to knowledge graph paths, producing interpretable hierarchical structures that align with ontologies, demonstrating promising results comparable to current state-of-the-art methods.

## Contribution

The paper proposes a novel end-to-end sequence-to-sequence approach for generating knowledge graph paths from text, emphasizing interpretability and hierarchical structure.

## Key findings

- Model achieves results comparable to state-of-the-art systems.
- Hierarchical, interpretable predictions align with ontologies.
- Proof-of-concept demonstrates feasibility of the approach.

## Abstract

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.