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Navigational and Rule-Based Languages for Graph Databases

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Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering (Reasoning Web 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9885))

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Abstract

One of the key differences between graph and relational databases is that on graphs we are much more interested in navigational queries. As a consequence, graph database systems are specifically engineered to answer these queries efficiently, and there is a wide body of work on query languages that can express complex navigational patterns.

The most commonly used way to add navigation into graph queries is to start with a basic pattern matching language and augment it with navigational primitives based on regular expressions. For example, the friend-of-a-friend relationship in a social network is expressed via the primitive (friend)+, which looks for paths of nodes connected via the friend relation. This expression can be then added to graph patterns, allowing us to retrieve, for example, all nodes A, B and C that have a common friend-of-a-friend.

But, in order to alleviate some of the drawbacks of isolating navigation in a set of primitives, we have recently witnessed an effort to study languages which integrate navigation and pattern matching in an intrinsic way. A natural candidate to use is Datalog, a well known declarative query language that extends first order logic with recursion, and where pattern matching and recursion can be arbitrarily nested to provide much more expressive navigational queries.

In this chapter we review the most common navigational primitives for graphs, and explain how these primitives can be embedded into Datalog. We then show current efforts to restrict Datalog in order to obtain a query language that is both expressive enough to express all these primitives, but at the same time feasible to use in practice. We illustrate how this works both over the base graph model and over the more general RDF format underlying the semantic web.

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Notes

  1. 1.

    These programs are sometimes referred to as stratified linear programs, or piecewise linear Programs [38].

  2. 2.

    To simplify the presentation in this chapter we only consider ground RDF graphs [24], i.e. RDF graphs which do not contain any blank nodes nor literals.

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Acknowledgments

We would like to thank Pablo Barceló, Jorge Pérez, Miguel Romero, Moshe Vardi, Egor Kostlyev and Leonid Libkin for helpful discussions on the topics of this chapter, as several of the results presented here where published in papers co-authored with them. The authors are supported by the Millennium Nucleus Center for Semantic Web Research Grant NC120004.

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Authors and Affiliations

  1. Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile

    Juan L. Reutter & Domagoj Vrgoč

  2. Center for Semantic Web Research, Santiago, Chile

    Juan L. Reutter & Domagoj Vrgoč

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  1. Juan L. Reutter
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  2. Domagoj Vrgoč
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Correspondence to Juan L. Reutter .

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Editors and Affiliations

  1. University of Aberdeen , Aberdeen, United Kingdom

    Jeff Z. Pan

  2. Free University of Bozen-Bolzano , Bolzano, Italy

    Diego Calvanese

  3. University of Technology Vienna , Vienna, Austria

    Thomas Eiter

  4. University of Oxford , Oxford, United Kingdom

    Ian Horrocks

  5. Stony Brook University , Stony Brook, New York, USA

    Michael Kifer

  6. University of Science and Technology , Hong Kong, China

    Fangzhen Lin

  7. University of Aberdeen , Aberdeen, United Kingdom

    Yuting Zhao

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Reutter, J.L., Vrgoč, D. (2017). Navigational and Rule-Based Languages for Graph Databases. In: Pan, J., et al. Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering. Reasoning Web 2016. Lecture Notes in Computer Science(), vol 9885. Springer, Cham. https://doi.org/10.1007/978-3-319-49493-7_3

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