Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.
One strong assumption mentioned above in the most RE methods is that a fixed (i.e., closed) set of relation types is given as an prior knowledge, thus no zero-shot learning capability (i.e. detecting new relations that did not occur during training) is required. Another commonality among these RE methods is that the relation set is usually not large. Here are some examples. The widely used ACE2005 has 11 32 coarse fine-grained relations; SemEval2010 Task8 has 19 relations; TAC-KBP2015 has 74 relations although it considers open-domain Wikipedia relations. Compared to that, KBQA usually has thousands of relations. Thus most RE approaches may not work well by directly being adapted to large number of relations or unseen relations. The relation embeddings in a low-rank tensor method were used @cite_3 . However it is still using supervised way to train their relation embeddings and relation set used in the experiments is still not large.
@cite_3
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Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11 , and a method leveraging pre-trained embeddings by 7 .