  Natural language question-answering over RDF data has received widespread
attention. Although there have been several studies that have dealt with a
small number of aggregate queries, they have many restrictions (i.e.,
interactive information, controlled question or query template). Thus far,
there has been no natural language querying mechanism that can process general
aggregate queries over RDF data. Therefore, we propose a framework called NLAQ
(Natural Language Aggregate Query). First, we propose a novel algorithm to
automatically understand a users query intention, which mainly contains
semantic relations and aggregations. Second, to build a better bridge between
the query intention and RDF data, we propose an extended paraphrase dictionary
ED to obtain more candidate mappings for semantic relations, and we introduce a
predicate-type adjacent set PT to filter out inappropriate candidate mapping
combinations in semantic relations and basic graph patterns. Third, we design a
suitable translation plan for each aggregate category and effectively
distinguish whether an aggregate item is numeric or not, which will greatly
affect the aggregate result. Finally, we conduct extensive experiments over
real datasets (QALD benchmark and DBpedia), and the experimental results
demonstrate that our solution is effective.
