# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 1 Introduction
In order to deliver the correct meaning of _spouseOf_, a pattern \"a daughter of \(\{arg1\}\) and \(\{arg2\}\)\" should be made. Since the phrase _\"a daughter of\"_ is located out of _'[PERSON]'_ and _'[PERSON]'_, such a pattern can not be generated from the sentence. Therefore, a more effective representation for patterns is needed to express dependencies of the words that are not located within entities. An entity pair can have more than two relations in general. Thus, a sentence that mentions both entities in a seed triple can express other relations that are different from the relation of the seed triple. Then, the patterns extracted from such sentences become useless in gathering new knowledge for the relation of the seed triple. For instance, assume that a triple \(\langle Eve,workFor,Selene\rangle\) is given as a seed knowledge. Since only entities are considered in generating patterns, _\"[arg1] is a daughter of [arg2]\"_ from the sentence _\"[PERSON] is a daughter of [PERSON] and [PERSON]\"_ becomes a pattern for a relation _workFor_, while the pattern does not deliver the meaning of _workFor_ at all. Therefore, it is important for generating high-quality patterns to filter out the pattern candidates that do not deliver the meaning of the relation in a seed triple. One possible solution to this problem is to define a confidence of a pattern candidate according to the relatedness between a pattern candidate and a target relation. Statistical information such as frequency of pattern candidates or co-occurrence information of pattern candidates and some predefined features has been commonly used as a pattern confidence in the previous work [6; 9]. However, such statistics-based confidence does not reflect semantic relatedness between a pattern and a target relation directly. That is, even when two entities co-occur very frequently to express the meaning of a relation, there could be also many cases in which the entities have other relations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Relation** & **Pattern** \\ \hline _childOf_ & [arg1] is a daughter of \(\{\)arg2\(\}\) \\ \hline _spouseOf_ & [arg1] and [arg2] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example patterns generated by a lexical pattern generator. Therefore, in order to determine if a pattern is expressing the meaning of a relation semantically correctly, the semantic relatedness between a pattern and a relation should be investigated. In this paper, we propose a novel but simple system for bootstrapping a knowledge base expressed in triples from a large volume of unstructured documents. Especially, we show that the dependencies between entities and semantic information can improve the performance over the previous approaches without much effort. For overcoming the limitations of lexical sequence patterns, the system expresses a pattern as a parse tree, not as a lexical sequence. Since a parse tree of a sentence presents deep linguistic analysis of the sentence and expresses long distance dependencies easily, the use of parse tree patterns results in higher performance in knowledge enrichment than lexical sequences. In addition, the deployment of a semantic confidence for parse tree patterns allows irrelevant pattern candidates to be filtered out. The semantic confidence between a pattern and a relation in a seed knowledge is defined as an average similarity between the words of the pattern and those of the relation. Among various similarity measurements, we adopt two common semantic similarity measurements: a WordNet-based similarity and a word embedding similarity. Generally, WordNet similarity shows plausible results but sometimes it suffers from out-of-vocabulary (OOV) problem [10]. Since patterns can contain many words that are not listed in WordNet, the similarity is supplemented by word similarity in a word embedding space. Thus, the final word similarity is the combination of similarity by WordNet and that in a word embedding space.