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When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation

Building a Knowledge Base from text corpora is useful for many applications such as question answering and web search. Since 2012, the Cold Start Knowledge Base Population (KBP) evaluation at the Text Analysis Conference (TAC) has attracted many participants. Despite the popularity, the Cold Start KBP evaluation has several problems including but not limited to the following two: first, each year’s assessment dataset is a pooled set of query-answer pairs, primarily generated by participating systems. It is well known to participants that there is pooling bias: a system developed outside of the official evaluation period is not rewarded for finding novel answers, but rather is penalized for doing so. Second, the assessment dataset, constructed with lots of human effort, offers little help in training information extraction algorithms which are crucial ingredients for the end-to-end KBP task. To address these problems, we propose a new unbiased evaluation methodology that uses existing component-level annotation such as the Automatic Content Extraction (ACE) dataset, to evaluate Cold Start KBP. We also propose bootstrap resampling to provide statistical significance to the results reported. We will then present experimental results and analysis.