In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more ﬂexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difﬁcult to ﬁgure out what an entity actually represents. Thus, matching the intended schema as speciﬁed by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a speciﬁc semantic meaning of the graph pattern, and discuss their practical applicability. Keywords Graph databases · Graph queries · Conceptual modeling · Pattern matching 1Introduction a dynamically growing collect-as-you-go manner. A good example is
Datenbank-Spektrum – Springer Journals
Published: May 31, 2018
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