Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (2020)

Abstract: Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.

## introduction relation classification (rc) is an important task in information extraction, aiming to extract the relation between two given entities based on their related context. rc has attracted increasing attention due to its broad applications in many downstream tasks, such as knowledge base construction @xcite and question answering @xcite . conventional supervised rc approaches can not satisfy the practical needs of the relation classification. in the real world, there exist massive amounts of fine-grained relations. and, the labeled relation types are limited, and each type usually has a certain number of labeled samples. naturally, it is prohibitive to generalize to new (unseen) relations (i.e., the model will fail when predicting a type with no training examples). for example, in figure 1 , basin country is an unseen relation type with no labeled sentence in the training stage. to this end, it is urgent for models to be able to extract relations in a zero-shot scenario. previous zero-shot relation classification (zsrc) approaches leverage transfer learning procedures by reading comprehension @xcite , textual entailment @xcite , and so on. however, those methods have to rely on artificial descriptive information to improve the understandability of relation types. inspired by the zero-shot learning in computer vision @xcite , it is natural to learn a mapping from the feature space of input samples to the semantic space such as class labels through a projection function. the hypothesis is to build the semantic connections between seen and unseen relations. conventional approaches usually leverage word embeddings @xcite of labels as a common semantic space. we argue that for relation classification, rich semantic knowledge is neglected in the relation labels space: implicit semantic connection with knowledge graph embedding. previous studies @xcite have shown that the knowledge graph embeddings (kges) of semantically similar relations are located near each other in the latent space. for instance, the relation place lived and nationality are more relevant, whereas the relation profession has less correlation with the former two relations. thus, it is natural to leverage this knowledge from kgs to build connections between seen and unseen relations. ## experiment in experiments, we want to explore: 1) whether embeddings based on kgs are more useful for the zsrc task than word embeddings? 2) what is the factor that can strengthen semantic representations in zsrc? ## conclusion and future work we have studied the zero-shot relation classification task and took the first step towards bridging symbolic reasoning with semantic representations. extensive experiments demonstrate the efficacy of our approach, revealing the advantages of knowledge graph embeddings and rules. in the future, we plan to exploit more efficient approaches to obtain symbolic rules an build end-to-end reasoning approaches for zero-shot tasks.