Abstract
We study relation extraction for knowledge
base (KB) enrichment. Specifically, we aim
to extract entities and their relationships from
sentences in the form of triples and map the
elements of the extracted triples to an existing
KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on
Named Entity Disambiguation (NED) to map
triples into the KB space. This way, NED errors may cause extraction errors that affect the
overall precision and recall. To address this
problem, we propose an end-to-end relation
extraction model for KB enrichment based on
a neural encoder-decoder model. We collect
high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based
attention model that captures multi-word entity names in a sentence. Our model employs
jointly learned word and entity embeddings to
support named entity disambiguation. Finally,
our model uses a modified beam search and
a triple classifier to help generate high-quality
triples. Our model outperforms state-of-theart baselines by 15.51% and 8.38% in terms of
F1 score on two real-world datasets.