  We present a new perspective on neural knowledge base (KB) embeddings, from
which we build a framework that can model symbolic knowledge in the KB together
with its learning process. We show that this framework well regularizes
previous neural KB embedding model for superior performance in reasoning tasks,
while having the capabilities of dealing with unseen entities, that is, to
learn their embeddings from natural language descriptions, which is very like
human's behavior of learning semantic concepts.
