QLiG: Query Like a Graph For Subgraph Matching

Abstract

A graph is a natural and flexible modeling approach to represent entities and relationships between them in the real world. A Knowledge Graph (KG) is a specialized graph with formal and structured representations of facts, relationships, annotated with semantic descriptions. Subgraph matching is one of the fundamental graph problems to identify relationships, interactions, and activities of interest within a large graph. A query specification is a collection of abstract components, operations, and constraints to express a pattern. The specification can be implemented in different ways based on the underlying data model. Various graph query specifications have been developed over the years that has led to the development of different open-sourced and vendor-specific query languages. Such specifications are modeled as an extension of relational algebra used in relational query languages such as SQL. Such approaches do not inherently support graph queries. There is a need to represent graph queries in terms of graph-based components to expedite the query construction by non-database experts. We present a graph-based query approach QLiG (pronounced cleeg), to perform subgraph matching in a Labeled Property Graph (LPG). QLiG provides required expressivity to represent a query graph in a natural way using high-level concepts such as path, structure, and constraints. We present the query specification, salient features, and a real-world use case to show functional examples.