Chinese Relation Patterns Mining for Knowledge Base Acceleration
Date Issued
2015
Date
2015
Author(s)
Chiu, Yen-Pin
Abstract
In recent years, structural knowledge base have attracted much attention in information retrieval and natural language processing. People rely on structural data to implement many applications that benefit the human life. With the growth of internet it is almost impossible for human editors to update all the world knowledge generated by people all around the world everyday to knowledge base. How to accelerate the construction of knowledge base becomes a critical issue known as knowledge base acceleration. Relation extraction technique can be the key part to accelerate the knowledge base construction progress and to extract the relation between entities. There are useful resources such as relation patterns which can denote the binary relation between two entities. However, there are few relation patterns resources available in Chinese information extraction. In this work, we present a Chinese relation patterns taxonomy for knowledge base acceleration. Each pattern in this taxonomy is semantically typed into YAGO properties and has its own confidence and entity types defined. We will describe the complete method to collect the Chinese corpora and to extract the Chinese relation patterns from them. Finally, we will examine the correctness of those patterns to evaluate the performance of the proposed pattern extraction method and analyze the errors occurred during the experiment. With the Chinese relation patterns taxonomy, many related works can be transferred from English to Chinese environments and further improve the usability and scale of Chinese knowledge bases.
Subjects
knowledge base
knowledge base acceleration
relation extraction
data mining
relation pattern
Type
thesis
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