# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 1 Introduction  
The World Wide Web contains abundant knowledge by virtue of the contributions of its great number of users and the knowledge is being utilized in diverse fields. Since ordinary users of the Web use, in general, a natural language as their major representation for generating and acquiring knowledge, unstructured texts constitute a huge proportion of the Web. Although human beings regard unstructured texts naturally, such texts do not allow machines to process or understand the knowledge contained within them. Therefore, these unstructured texts should be transformed into a structural representation to allow them to be machine-processed. The objective of knowledge base enrichment is to bootstrap a small seed knowledge base to a large one. In general, a knowledge base consists of triples of a subject, an object and their relation. Existing knowledge bases are not perfect in two respects of relations and triples (instances). Note that even a massive knowledge base like DBpedia, freebase or YAGO is not perfect for describing all relations between entities in the real world. However, this problem is often solved by restricting target applications or knowledge domains [1; 2]. Another problem is lack of triples. Although existing knowledge bases contain a massive amount of triples, they are still far from perfect compared to infinite real-world facts. This problem can be solved only by creating triples infinitely. Especially, according to the work by [PERSON] [3], the cost of making a triple manually is 15 to 250 times more expensive than that of automatic method. Thus, it is very important to generate triples automatically. As mentioned above, a knowledge base uses a triple representation for expressing facts, but new knowledge usually comes from unstructured texts written in a natural language. Thus, knowledge enrichment aims at extracting as many entity pairs for a specific relation fromunstructured texts as possible. From this point of view, pattern-based knowledge enrichment is one of the most popular methods among various implementations of knowledge enrichment. Its popularity comes from the fact that it can manage diverse types of relations and the patterns can be interpreted with ease. In pattern-based knowledge enrichment where a relation and an entity pair connected by the relation are given as a seed knowledge, it is assumed that a sentence that mentions the seed entity pair contains a lexical expression for the relation and this expression becomes a pattern for extracting new knowledge for the relation. Since the quality of the newly extracted knowledge is strongly influenced by that of the patterns, it is important to generate high-quality patterns. The quality of patterns depends primarily on the method used to extract the tokens in a sentence and to measure the confidence of pattern candidates. Many previous studies such as NELL [4], ReVerb [5], and BOA [6] adopt lexical sequence information to generate patterns [7; 8]. That is, when a seed knowledge is expressed as a triple of two entities and their relation, an intervening lexical sequence between the two entities in a sentence becomes a pattern candidate. Such lexical patterns have been reported to show reasonable performances in many knowledge enriching systems [4; 6]. However, they have obvious limitations that (i) they fail in detecting long distance dependencies among words in a sentence, and (ii) a lexical sequence does not always deliver the correct meaning of a relation. Assume that a sentence _\"[PERSON] is a daughter of [PERSON] and [PERSON]\"_ is given. A simple lexical pattern generator like BOA harvests from this sentence the patterns shown in Table 1 by extracting a lexical sequence between two entities. The first pattern is for a relation _childOf_, and is adequate for expressing the meaning of a parent-child relation. Thus, it can be used to extract new triples for _childOf_ from other sentences. However, the second pattern, \"\(\{arg1\}\) and \(\{arg2\}\)\" fails in delivering the meaning of a relation _spouseOf_.