Graph databases are enjoying enormous popularity, through both their RDF and Property Graphs (PG) incarnations, in a variety of applications. To query graphs, query languages provide structured, as well as unstructured primitives. While structured queries allow expressing precise information needs, they are unsuited for exploring unfamiliar datasets, as they require prior knowledge of the schema and structure of the dataset. Prior research on keyword search in graph databases do not suffer from this limitation. However, keyword queries do not allow expressing precise search criteria when users do know some. For example, consider the example query in the figure above – it has a structured component (finding the three sets of US entrepreneurs, French entrepreneurs and French politicians resp.) and an unstructured component (finding the connections between the three sets). This tutorial (1.5 hours) builds a continuum between structured graph querying through languages such as SPARQL and GPML, a recently proposed standard for PG querying, on one hand, and graph keyword search, on the other hand to enable expressing and answering such queries. We conclude by discussing the open challenges and future work directions.

Outline

Part I (10 minutes): Introduction to graph data models. ConnectionLens and applications.

Part II (10 minutes): Means of querying the graphs: structured and unstructured search.

Part III (30 minutes): Structured discussion on unstructured search techniques.

Part IV (25 minutes): Language mechanisms for integrating both complex structured querying and powerful methods to search for connections users do not know in advance.

Part V (5 minutes): Open challenges and future work directions.

Q&A (10 minutes)

Slides: Link