There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks. The conventional mechanism to retrieve facts in KGs usually involves three steps: entity span detection, entity disambiguation, and relation classification. However, this approach requires additional labels for training each of the three subcomponents in addition to pairs of input texts and facts, and also may accumulate errors propagated from failures in previous steps. To tackle these limitations, we propose a simple knowledge retrieval framework, which directly retrieves facts from the KGs given the input text based on their representational similarities, which we refer to as Direct Fact Retrieval (DiFaR). Specifically, we first embed all facts in KGs onto a dense embedding space by using a language model trained by only pairs of input texts and facts, and then provide the nearest facts in response to the input text. Since the fact, consisting of only two entities and one relation, has little context to encode, we propose to further refine ranks of top-k retrieved facts with a reranker that contextualizes the input text and the fact jointly. We validate our DiFaR framework on multiple fact retrieval tasks, showing that it significantly outperforms relevant baselines that use the three-step approach.

翻译：基于知识图谱的各类自然语言处理/理解任务近期引发了广泛研究兴趣。传统知识图谱事实检索机制通常包含三个步骤：实体跨度检测、实体消歧和关系分类。然而，该方法除需要输入文本与事实的配对数据外，还需为每个子组件标注额外训练标签，同时存在错误级联传播的问题。为克服上述局限性，我们提出一种简洁的知识检索框架——直接事实检索（DiFaR），该框架基于表征相似性，直接根据输入文本从知识图谱中检索事实。具体而言，我们首先利用仅通过输入文本与事实配对数据训练的语言模型，将知识图谱中所有事实嵌入至稠密向量空间，随后响应输入文本返回最邻近事实。由于包含两个实体与单一关系的事实缺乏丰富编码上下文，我们进一步提出通过联合上下文理解输入文本与事实的重排序器，优化Top-K检索事实的排序结果。我们在多个事实检索任务上验证了DiFaR框架的有效性，实验表明其性能显著优于采用三步法的相关基线模型。