# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 2 Related Work  
In recent years, relation extraction, like big data and cloud computing [14; 15], has attracted considerable interest from researchers. The early works mainly focused on the handcrafted feature-based models [16; 17; 18] and kernel-based models [19; 20]. These methods rely on the NLP tools, which unavoidably leads to error propagation or accumulation. With the development of neural network technology in recent years, a number of relation extraction researches have been proposed to utilize the neural network models [21]. These methods can alleviate the model's dependence on accurate feature matching and have achieved great progress for relation extraction. [PERSON] et al. [22] utilized a simple CNN model that does not even have a pooling layer to extract the features of sentences, which is the first attempt that using a CNN model to extract relations of entity pairs. [PERSON] et al. [7] incorporated word embedding and position embedding as the input of a CNN model to generate the sentence features. Combining the word embedding and position embedding can obtain more semantic information of the relation mentions. Many works focus on improving the performances of the neural network methods. However, most of these supervised methods require large-scale labeled training data which is expensive to obtain. In order to address this problem, distant supervision is proposed to automatically generate large-scale labeled training data [10]. The distant supervision is based on the assumption that there is a relation between an entity pair in a knowledge graph, all sentences in a corpus containing the entity pair express this relation. However, this assumption is too strong in practice due to the wrong label problem. To alleviate the impact of the wrong labeled instances in the distant supervision learning, some works have been proposed to use the multi-instance learning method [23; 24], which gathers the sentences mentioning the same entity pair into a bag to share a label. The attention mechanism was also introduced in distantly supervised relation extraction in recent years, which can let the neural network models focus on the informative sentences. [PERSON] et al. [25] proposed an attention guided graph convolutional networks to selectively attend to the relevant substructures of dependency trees useful for the relation extraction task. [PERSON] et al. [26] proposed the sentence-level attention over instances in a bag to select the important sentences. Some works combine the word-level and sentence-level attention mechanism to further improve the performance of distantly supervised relation extraction [27; 28]. In addition to utilizing the semantic information from sentences, some methods also introduce external information to augment existing relation extraction models. Such as the text descriptions of entities, which can provide helpful supplementary information for relation classification. [PERSON] et al. [29] proposed a new neural relation classification method to integrate the text descriptions of the entities into a deep CNN model for relationclassification. [PERSON] et al. [30] proposed to use the side information, such as entity type and relation alias, to enhance the performance of relation extraction. [PERSON] et al. [31] proposed a joint representation learning framework to generate the mutual attention between knowledge graphs and texts. This reciprocal attention mechanism can highlight the important features and perform better knowledge graph completion and relation extraction. [PERSON] et al. [32] modeled the relation path between two entities in a knowledge graph to encode the relational semantics from both direct sentences and inference chains. Despite the previous works have achieved the state-of-the-art performances, most of these methods consider only the textual information of entity mentions or surface lexical features present in sentences.