论文标题
忠实的知识基础查询嵌入
Faithful Embeddings for Knowledge Base Queries
论文作者
论文摘要
理想知识库（KB）的演绎关闭完全包含KB可以回答的逻辑查询。但是，实际上，KBS既不完整又指定过多，因此未能回答一些具有现实世界答案的查询。最近已经提出了\ emph {query嵌入}（QE）技术，其中KB实体和KB查询在嵌入空间中共同表示，从而支持KB推断中的放松和概括。但是，本文的实验表明，量化量化宽松系统可能不同意不需要概括或放松的答案的推论推理。我们使用一种新颖的量化宽松方法来解决这个问题，该方法更忠实于演绎推理，并表明这会在复杂的查询中获得更好的性能，从而使KBS不完整。最后，我们表明将这个新的量化量化量化模块插入神经问题的系统中会导致对最先进的实质性改进。
The deductive closure of an ideal knowledge base (KB) contains exactly the logical queries that the KB can answer. However, in practice KBs are both incomplete and over-specified, failing to answer some queries that have real-world answers. \emph{Query embedding} (QE) techniques have been recently proposed where KB entities and KB queries are represented jointly in an embedding space, supporting relaxation and generalization in KB inference. However, experiments in this paper show that QE systems may disagree with deductive reasoning on answers that do not require generalization or relaxation. We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs. Finally we show that inserting this new QE module into a neural question-answering system leads to substantial improvements over the state-of-the-art.
