Query Processing over Multiple Knowledge Bases and Text Documents
Marika Nakano, Toshiyuki Amagasa · 2021
Recently, knowledge bases, which represent general knowledge as graphs, have attracted much attention and are used in various fields. As a consequence, the number of knowledge bases have been growing and more than 600,000 datasets exist with different targets in wide spectrum of domains. Thus, it is desirable to make it possible to process queries over different knowledge bases in order for the users to get more integrated information. However, it is still difficult because one needs to cope with different vocabularies. Besides, there is another problem in knowledge bases that new facts tend to be missing, while we can get latest information from text documents, e.g., new sites. In this paper, we propose a method for query processing over multiple knowledge bases and text documents. We apply open information extraction (OIE) over text documents to extract knowledge graphs. To deal with the heterogeneity of vocabulary and schema, we perform distributed queries to multiple knowledge bases using mediator/wrapper approach. Besides, we introduce different strategies to join triples retrieved from different knowledge graphs of different RDF vocabulary. The evaluations show that our approach can improve the coverage of query results by using information from multiple knowledge bases and information from texts.