Commonsense mining as knowledge base completion? A study on the impact of novelty
This work addresses the challenge of mining novel commonsense knowledge for AI systems, but it is incremental as it builds on prior research and focuses on a specific aspect.
The study investigates whether knowledge base completion models can mine commonsense knowledge from raw text, focusing on novelty as a key factor, and finds that a simple baseline method outperforms previous state-of-the-art in predicting more novel triples.
Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by the recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel.