Chinese Relation Patterns Mining with High Coverage for Knowledge Base Acceleration and Completion
Date Issued
2016
Date
2016
Author(s)
Wei, Sheng-Lun
Abstract
With the rapid development of the Internet in recent years, people can get infor-mation from it through different sources such as online news, social network, and fo-rums. A lot of information is created by people every day and some of them can be col-lected, comprehended, and turned into knowledge by human beings. Knowledge base is a way that people store those information with structural format. However, it’s hard to keep knowledge base up-to-date because of the wide gap between limited editors and numerous information of entities. Knowledge base acceleration is a critical issue which focus on accelerating the construction of knowledge base. In addition, relation patterns are useful for knowledge base acceleration. However, there are no resources available in languages beyond English. In this study, we present a workflow for building relation pattern extraction system with high coverage for knowledge base acceleration and knowledge base completion. Our properties is based on the properties in DBpedia knowledge base. We will discuss many details of our method including corpus pre-processing, instance retrieval, and pat-tern extraction. Finally, we evaluate our relation patterns by human annotators and dis-cuss features that may affect the performance of the relation patterns. With Chinese relation patterns, many related work can be utilized in Chinese by transferring from English environment to Chinese environment. Other languages may also use our method to build their own relation pattern resources.
Subjects
knowledge base, knowledge base acceleration, knowledge base completion, relation extraction, relation pattern, data mining
Type
thesis
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