Textual Entailment for Effective Triple Validation in Object Prediction
Andrés García-Silva Cristian Berrío José Manuel Gómez-Pérez
Knowledge base population seeks to expand knowledge graphs with facts that
are typically extracted from a text corpus. Recently, language models
pretrained on large corpora have been shown to contain factual knowledge that
can be retrieved using cloze-style strategies. Such approach enables zero-shot
recall of facts, showing competitive results in object prediction compared to
supervised baselines. However, prompt-based fact retrieval can be brittle and
heavily depend on the prompts and context used, which may produce results that
are unintended or hallucinatory.We propose to use textual entailment to
validate facts extracted from language models through cloze statements. Our
results show that triple validation based on textual entailment improves
language model predictions in different training regimes. Furthermore, we show
that entailment-based triple validation is also effective to validate candidate
facts extracted from other sources including existing knowledge graphs and text
passages where named entities are recognized.