Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
This addresses the challenge of knowledge extraction from unstructured text for applications like semantic web and data integration, though it is incremental.
The paper tackles the problem of converting natural language text into structured triples compliant with a knowledge graph vocabulary, achieving competitive F1-measures on three datasets.
We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings. Our model learns the mapping from natural language text to triple representation in the form of subject-predicate-object using the selected knowledge graph vocabulary. Experiments on three different data sets show that we achieve competitive F1-Measures over the baselines using our simple yet effective approach. A demo video is included.