# Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning
## 2 Related Work
### Relation Extraction  
Relation extraction is one of the most important tasks in NLP (natural language processing). Many efforts have been invested in relation extraction. Relationship extraction is transformed into relationship classification [19]. One related work was proposed by [PERSON] and [PERSON] [20] and utilizes many features derived from external corpora for a support vector machine (SVM) classifier. Recently, deep neural networks have been shown to learn underlying features automatically and have been used in the literature. The most representative progress was made by [PERSON] et al., who utilized convolutional neural networks (CNNs) for relation classification [1,21]. While CNNs are not suitable for learning long-distance semantic information, the RNN (recurrent neural network) is often used for text processing [22]. One related work was proposed by [PERSON] and [PERSON], which employed bidirectional RNN to learn patterns of relations from raw text data [23]. Although the bidirectional RNN has access to both past and future context information, the range of context is limited due to the vanishing gradient problem [24]. To overcome this problem, long short-term memory (LSTM) units were introduced by [PERSON] and [PERSON] [25]. Moreover, the GRU (gated recurrent unit) proposed by [PERSON] et al. is a good variant of the LSTM network [26]. It is simpler and more efficient than the LSTM network, so the method of this paper builds on the bidirectional GRU. Most of these methods are supervised relation extraction, which is time-consuming and labor intensive. To address this issue, [PERSON] et al. align plain text with free-base by distance supervision [27]. However, distance supervision inevitably encounters the wrong labeling problem. To alleviate the wrong labeling problem, [PERSON] et al. model distant supervision for relation extraction as a multi-instance single-label problem [28], and [PERSON] et al. adopt multi-instance multilabel learning in relation extraction [29,30]. However, all of the feature-based methods strongly depend on the quality of the features generated by NLP tools, which will suffer from the error propagation problem and the difficulty of applying the multi-instance learning strategy of conventional methods in neural network models. Therefore, [PERSON] et al. combine at-least-one multi-instance learning with a neural network model to extract relations on distant supervision data [31]. However, they assume that only one sentence is active for each entity pair, and it will therefore lose a large amount of rich information contained in those neglected sentences. Hence, [PERSON] et al. propose sentence-level attention over multiple  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Language** & **Online API (application programming interface)** & **Status** & **Commercial** \\ \hline Wikify! & English & Yes & Active & No \\ TAGME & English & Yes & Active & No \\ Spotlight & English & Yes & Active & No \\ CMEL & Chinese & no & update to 2014 & No \\ Yuan et al. & Chinese & no & update to 2015 & No \\ CN-EL & Chinese & Yes & Active & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of entity extraction approaches.  
instances, which can utilize all informative sentences [32]. Since each word in a sentence has a different importance to the semantic expression of the sentence, this paper also uses the word-level attention. In recent years, the research of graph neural network has become a hot topic in the field of deep learning, [PERSON] et al. use graph neural newtwork extract relation, and achieve good results.  
In this paper, we will extract the relation between cars from unstructured Chinese text. For example, given the Chinese text \"Volkswagen's two classic b-class cars Magotan and Passat have been occupying a large share of domestic automobile sales\", we can extract that the semantic relation between \"Magotan\" and \"Passat\" is \"Same Level\". In this experiment, we define four semantic relations: \"Same Level\", \"Homology\", \"Subordinate\" and \"Unknown\".