Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. Due to their complex schemas and variational information descriptions, it becomes very hard for users to formulate a query that can be properly processed by the existing systems. We argue that for a user-friendly graph query engine, it must support various kinds of transformations such as synonym, abbreviation, and ontology. Furthermore, the derived query results must be ranked in a principled manner. In this paper, we introduce a novel framework enabling schemaless and structureless graph querying (SLQ), where a user need not describe queries precisely as required by most databases. The query engine is built on a set of transformation functions that automatically map keywords and linkages from a query to their matches in a graph. It automatically learns an effective ranking model, without assuming manually labeled training examples, and can efficiently return top ranked matches using graph sketch and belief propagation. The architecture of SLQ is elastic for "plug-in" new transformation functions and query logs. Our experimental results show that this new graph querying paradigm is promising: It identifies high-quality matches for both keyword and graph queries over real-life knowledge graphs, and outperforms existing methods significantly in terms of effectiveness and efficiency.
In REF , a set of transformation functions are used to map attributes of nodes and edges appearing in a graph query to matches in the data graph, and a ranking model for query answers is learnt using automatically generated training instances and the query log.
Schemaless and Structureless Graph Querying
PVLDB
PVLDB
Computer Science