Incorporating multiple knowledge sources is proven to be beneficial for
answering complex factoid questions. To utilize multiple knowledge bases (KB),
previous works merge all KBs into a single graph via entity alignment and
reduce the problem to question-answering (QA) over the fused KB. In reality,
various link relations between KBs might be adopted in QA over multi-KBs. In
addition to the identity between the alignable entities (i.e. full link),
unalignable entities expressing the different aspects or types of an abstract
concept may also be treated identical in a question (i.e. partial link). Hence,
the KB fusion in prior works fails to represent all types of links, restricting
their ability to comprehend multi-KBs for QA. In this work, we formulate the
novel Multi-KB-QA task that leverages the full and partial links among multiple
KBs to derive correct answers, a benchmark with diversified link and query
types is also constructed to efficiently evaluate Multi-KB-QA performance.
Finally, we propose a method for Multi-KB-QA that encodes all link relations in
the KB embedding to score and rank candidate answers. Experiments show that our
method markedly surpasses conventional KB-QA systems in Multi-KB-QA, justifying
the necessity of devising this task.

利用多个知识库融合的全连接和部分连接，提出了用于多知识库问答的新任务，并构建了多样化的链接和查询类型基准来有效评估多知识库问答性能。通过将所有链接关系编码到知识库嵌入中，我们提出了一种用于多知识库问答的方法，其在实验中显著优于传统的知识库问答系统，验证了设计这个任务的必要性。