研究概述

来自Data Management Lab
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gStore: A Graph-based RDF Triple Store

gStore is a graph-based RDF data management system (or what is commonly called a “triple store”) that maintains the graph structure of the original RDF data. Its data model is a labeled, directed multiedge graph, where each vertex corresponds to a subject or an object. We also represent a given SPARQL query by a query graph Q. Query processing involves finding subgraph matches of Q over the RDF graph G. gStore incorporates an index over the RDF graph (called VS*-tree) to speed up query processing. VS*-tree is a heightbalanced tree with a number of associated pruning techniques to speed up subgraph matching.

gStore is an open source project. The newest version is released at Github. More details can be found here.

gAnswer: A Natural Language Qustion Answering System over Knowledge Graphs

As more and more structured data become available on the web, the question of how end users can access this body of knowledge becomes of crucial importance. Although SPARQL is a standard way to access RDF data, it remains tedious and difficult for end users because of the complexity of the SPARQL syntax and the RDF schema. Therefore, RDF question/answering (Q/A) systems have received wide attention in both NLP (natural language processing) and database areas. In this project, we propose a graph data-driven solution to address this issue. Specifically, we transform a natural language question to a semantic query graph. Then, we find the subgraph matches of the semantic query graph over knowledge graphs. The matches are the results to users' questions. Furthermore, we resolve the ambiguity of natural language phrases at the time when the matches are found.

We host an online demo of this project. gAnswer joined QALD-4 competition and it was ranked at the second one in the precision and recall. gAnswer also joined QALD-6. More details about gAnswer can be found here.