A Novel Online Encyclopedia-Oriented Approach for Large-Scale Knowledge Base Construction

Abstract

In the process of constructing large-scale knowledge base, manual-based construction approach lacks efficiency as well as flexibility. Therefore, automatically extracting of massive knowledge from online encyclopedia has attracted attention from an increasing number of scholars. Current research is mainly focused on the extracting of data from English online encyclopedia, whereas research about knowledge extraction from Chinese or other language data sources is rare. For such reason, the present paper proposes an automatic construction scheme for large-scale knowledge base based on Chinese online Encyclopedia. (i)In the first phase of the scheme, self-expanded learning is performed on the semantic relations between subjects and objects among the knowledge triples. (ii)In the second phase, semantic relations between the marked attributes and their entities is predicted using Conditional Random Fields (CRFs) and Support vector machine (SVM) classifier. A large-scale knowledge base is automatically constructed based on the scheme, and the experiment results indicate that the scheme possesses feasibility and effectiveness.