The authors propose a divide-and-conquer based solution over gStore, an existing RDF search engine, to process property path query on large scale graph data. In proposed solution, regular expression is partitioned within the path query and then preprocess strings of fixed length. The authors handle the search over those subqueries of wildcards. The proposed method is able to filter lots of unpromising search and efficient on solving the regular path match problem over large scale graph data. The corresponding experiments on DBpedia and LUBM confirm that proposed method can response for queries in seconds on average.

A framework to transform natural language questions into computer-understoodable structured queries is presented. The authors propose to use query semantic graph to represent the semantics in Chinese questions, and adopt predicate and entity disambiguation to match the query graph to the schema of a knowledge base. The authors collect a benchmark of 42 frequently-asked questions randomly sampled from 3 categories of Baidu Knows, including person, location and organization. Experiment results show that proposed framework can effectively convert natural language questions into SPARQL queries, and lay a good foundation for the next generation of intelligent question answering systems.