WDAquacore0: A Question Answering Component for the Research Community We describe and present a new Question Answering (QA) component that can be easily used by the QA research community. It can be used to answer questions over DBpedia and Wikidata. The language support over DBpedia is restricted to English, while it can be used to answer questions in 4 different languages over Wikidata namely English, French, German and Italian. Moreover it supports both full natural language queries as well as keyword queries. We describe the interfaces to access and reuse it and the services it can be combined with. Moreover we show the evaluation results we achieved on the QALD7 benchmark.

Datadriven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs Abstract   As increasingly more semantic realworld data is stored in knowledge graphs, providing intuitive and effective query methods for endusers is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF questionanswering (QA) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel datadriven graph similarity framework for RDF QA to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for samedistance graph elements and equip the process with an edge associationaware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms stateoftheart methods significantly.