# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 2 Related Work  
### Knowledge Base Enrichment  
A number of previous studies addressed the enrichment of knowledge bases [11; 12; 13; 14]. Among the methods proposed in these studies, relation extraction is one of the most popular methods [4; 6], because natural language is a major representation of knowledge on the Web. A lexical sequence of words as a pattern and the pattern frequency as a filtering method are commonly used in relation extraction [7]. [PERSON] et al. proposed a system called NELL (Never-Ending Language Learner) which performs learning by reading [4]. NELL enriches its own seed knowledge base with various knowledge extraction modules. Among the modules, CPL (Coupled Pattern Learner) learns contextual patterns from unstructured texts. When a sentence is given in which two entities of a seed knowledge exist, CPL treats an intervening sequence of words between the entities as a pattern candidate. In order to choose confident patterns from pattern candidates, it utilizes some statistical information and coupling constraints between relations or other knowledge extraction modules. BOA (BOotstrapping linked datA) proposed by [PERSON] et al. [6] extracts pattern candidates in a similar way to NELL. BOA measures the confidence of a pattern candidate using three statistical features of support, typicity, and specificity. Both NELL and BOA reported plausible performance, but both suffer from the weakness of lexical patterns as in Table 1.  
Some researchers tried to improve the performance of relation extraction with deep NLP techniques [9]. [PERSON] et al. presented WOE (Wikipedia-based Open Extractor), which uses dependency relation of words in a sentence to improve precision of patterns from long sentences [15]. They defined the shortest dependency path of a parse tree as a pattern. Then, they filtered out irrelevant patterns with a classification function based on pattern frequency. As a result, they achieved improved performance over lexical patterns. However, even the studies that adopt deep NLP analyses miss some valuable information needed to determine the correctness of patterns since they depend solely on frequency information of patterns. In fact, the correctness of a pattern should be determined by semantic relatedness between the pattern and the target relation, not by the frequency of the pattern. The frequency reflects just a little of the semantic relatedness indirectly. Recently, a neural network became a notable approach to enriching knowledge bases. A neural network provides a uniform framework for end-to-end modeling or joint modeling. This helps reduce the error propagation caused by the combination of several subtasks. [PERSON] et al. presented a Chinese encyclopedia knowledge base framework for extracting and verifying new facts [16]. In this work, relation extraction is formulated as a slot filling task that determines the values for entities and their attributes from a text. They adopted a BiLSTM [17] and an attention layer to construct their relation extractor. [PERSON] et al. proposed an end-to-end model for extracting and canonicalizing triples [18]. They reduced error propagation by jointly learning two tasks of extracting triples from sentences and mapping them into an existing knowledge base. [PERSON] et al. tried to extract biomedical entities and their relations [14]. Their model was trained to extract biomedical entities as well as their relations simultaneously. [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [21] and [PERSON] et al. [22] have used an extended version of CNN, so-called PCNN (piecewise CNN), for learning features automatically. [PERSON] et al. [23], [PERSON] et al. [24] and [PERSON] et al. [25] also adopted a neural network-based approach. Neural network models show, in general, superior performance to other methods in knowledge base enrichment. However, it is difficult to interpret the rationale behind new triples from neural network models.