ITEM: Extract and Integrate Entities from Tabular Data to RDF Knowledge Base

Xiaoyan Guo, Yueguo Chen, Jinchuan Chen, Xiaoyong Du

Abstract: Many RDF Knowledge Bases are created and enlarged by mining and extracting web data. Hence their data sources are limited to social tagging networks, such as Wikipedia, WordNet, IMDB, etc., and their precision is not guaranteed. In this paper, we propose a new system, ITEM, for extracting and integrating entities from tabular data to RDF knowledge base. ITEM can efficiently compute the schema mapping between a table and a KB, and inject novel entities into the KB. Therefore, ITEM can enlarge and improve RDF KB by employing tabular data, which is assumed of high quality. ITEM detects the schema mapping between table and RDF KB only by tuples, rather than the table’s schema information. Experimental results show that our system has high precision and good performance.