research-article

TriAL: A Navigational Algebra for RDF Triplestores

Authors:

Juan L. Reutter,

Domagoj VrgočAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 43, Issue 1

Article No.: 5, Pages 1 - 46

https://doi.org/10.1145/3154385

Published: 23 March 2018 Publication History

Abstract

Navigational queries over RDF data are viewed as one of the main applications of graph query languages, and yet the standard model of graph databases—essentially labeled graphs—is different from the triples-based model of RDF. While encodings of RDF databases into graph data exist, we show that even the most natural ones are bound to lose some functionality when used in conjunction with graph query languages. The solution is to work directly with triples, but then many properties taken for granted in the graph database context (e.g., reachability) lose their natural meaning.

Our goal is to introduce languages that work directly over triples and are closed, i.e., they produce sets of triples, rather than graphs. Our basic language is called TriAL, or Triple Algebra: it guarantees closure properties by replacing the product with a family of join operations. We extend TriAL with recursion and explain why such an extension is more intricate for triples than for graphs. We present a declarative language, namely a fragment of datalog, capturing the recursive algebra. For both languages, the combined complexity of query evaluation is given by low-degree polynomials. We compare our language with previously studied graph query languages such as adaptations of XPath, regular path queries, and nested regular expressions; many of these languages are subsumed by the recursive triple algebra. We also provide an implementation of recursive TriAL on top of a relational query engine, and we show its usefulness by running a wide array of navigational queries over real-world RDF data, while at the same time testing how our implementation compares to existing RDF systems.

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References

[1]

S. Abiteboul, R. Hull, and V. Vianu. 1995. Foundations of Databases. Addison-Wesley.

Digital Library

[2]

R. Angles. 2012. A comparison of current graph database models. In Proceedings of the International Conference on Data Engineering (ICDE’12). 171--177.

Digital Library

[3]

Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan L. Reutter, and Domagoj Vrgoc. 2016. Foundations of modern graph query languages. Retrieved from http://arxiv.org/abs/1610.06264.

[4]

R. Angles and C. Gutierrez. 2008. Survey of graph database models. Comput. Surveys 40, 1 (2008).

Digital Library

[5]

Renzo Angles, Arnau Prat-Pérez, David Dominguez-Sal, and Josep-Lluis Larriba-Pey. 2013. Benchmarking database systems for social network applications. In Proceedings of the 1st International Workshop on Graph Data Management Experiences and Systems (GRADES’13).

Digital Library

[6]

K. Anyanwu and A. P. Sheth. 2003. ρ-queries: Enabling querying for semantic associations on the semantic web. In Proceedings of the 12th International World Wide Web Conference (WWW’03). 690--699.

Digital Library

[7]

Marcelo Arenas, Georg Gottlob, and Andreas Pieris. 2014. Expressive languages for querying the semantic web. In Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS’14). 14--26.

Digital Library

[8]

M. Arenas and J. Pérez. 2011. Querying semantic web data with SPARQL. In Proceedings of the Symposium on Principles of Database Systems (PODS’11). 305--316.

Digital Library

[9]

Guillaume Bagan, Angela Bonifati, Radu Ciucanu, George H. L. Fletcher, Aurélien Lemay, and Nicky Advokaat. 2017. gMark: Schema-driven generation of graphs and queries. IEEE Trans. Knowl. Data Eng. 29, 4 (2017), 856--869.

Digital Library

[10]

P. Barceló, J. Pérez, and J. L. Reutter. 2012. Relative expressiveness of nested regular expressions. In Proceedings of the American mathematical Society (AMW’12). 180--195.

[11]

P. Barceló, J. Pérez, and J. L. Reutter. 2013. Schema mappings and data exchange for graph databases. In Proceedings of the International Conference on Database Theory (ICDT’13).

Digital Library

[12]

Meghyn Bienvenu, Magdalena Ortiz, and Mantas Simkus. 2015. Navigational queries based on frontier-guarded datalog: Preliminary results. In Proceedings of the Alberto Mendelzon International Workshop on Foundations of Data Management. 162.

[13]

Christian Bizer and Andreas Schultz. 2009. The Berlin sparql benchmark. (2009).

[14]

Pierre Bourhis, Markus Krötzsch, and Sebastian Rudolph. 2015. Reasonable highly expressive query languages. In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI’15). 2826--2832.

Digital Library

[15]

D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Y. Vardi. 2000. Containment of conjunctive regular path queries with inverse. In Proceedings of the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR). 176--185.

Digital Library

[16]

D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Y. Vardi. 2002. Rewriting of regular expressions and regular path queries. J. Comput. Syst. Sci. 64, 3 (2002), 443--465.

Digital Library

[17]

D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Y. Vardi. 2003. Reasoning on regular path queries. ACM SIGMOD Rec. 32, 4 (2003), 83--92.

Digital Library

[18]

M. Consens and A. O. Mendelzon. 1990. GraphLog: A visual formalism for real life recursion. In Proceedings of the 9th ACM Symposium on Principles of Database Systems (PODS’90). 404--416.

Digital Library

[19]

I. Cruz, A. O. Mendelzon, and P. Wood. 1987. A graphical query language supporting recursion. In Proceedings of the ACM Special Interest Group on Management of Data 1987 Annual Conference (SIGMOD’87). 323--330.

Digital Library

[20]

P. Cudré-Mauroux and S. Elnikety. 2011. Graph data management systems for new application domains. Proceedings of the VLDB Endowment (PVLDB) 4, 12 (2011), 1510--1511.

Digital Library

[21]

Evgeny Dantsin, Thomas Eiter, Georg Gottlob, and Andrei Voronkov. 2001. Complexity and expressive power of logic programming. ACM Comput. Surv. 33, 3 (2001), 374--425.

Digital Library

[22]

W. Fan, J. Li, S. Ma, N. Tang, and Y. Wu. 2010. Graph pattern matching: From intractable to polynomial time. Proceedings of the VLDB Endowment (PVLDB) 3, 1 (2010), 264--275.

Digital Library

[23]

W. Fan, J. Li, S. Ma, N. Tang, and Y. Wu. 2011. Adding regular expressions to graph reachability and pattern queries. In Proceedings of the 27th International Conference on Data Engineering (ICDE’11). 39--50.

Digital Library

[24]

George H. L. Fletcher, Marc Gyssens, Dirk Leinders, Dimitri Surinx, Jan Van den Bussche, Dirk Van Gucht, Stijn Vansummeren, and Yuqing Wu. 2015. Relative expressive power of navigational querying on graphs. Info. Sci. 298 (2015), 390--406.

Digital Library

[25]

G. H. L. Fletcher, M. Gyssens, D. Leinders, J. Van den Bussche, D. Van Gucht, S. Vansummeren, and Y. Wu. 2011. Relative expressive power of navigational querying on graphs. In Proceedings of the International Conference on Database Theory (ICDT’11). 197--207.

Digital Library

[26]

G. H. L. Fletcher, M. Gyssens, D. Leinders, J. Van den Bussche, D. Van Gucht, S. Vansummeren, and Y. Wu. 2012. The impact of transitive closure on the boolean expressiveness of navigational query languages on graphs. In Proceedings of the FoIKS. 124--143.

Digital Library

[27]

G. Gottlob, E. Grädel, and H. Veith. 2002. Datalog LITE: A deductive query language with linear time model checking. ACM TOCL 3, 1 (2002), 42--79.

Digital Library

[28]

G. Gottlob and C. Koch. 2004. Monadic datalog and the expressive power of languages for web information extraction. J. ACM 51, 1 (2004), 74--113.

Digital Library

[29]

Andrey Gubichev, Srikanta J. Bedathur, and Stephan Seufert. 2013. Sparqling kleene: Fast property paths in RDF-3X. In Proceedings of the International Workshop on Graph Data Management Experiences and Systems (GRADES’13). 14.

Digital Library

[30]

Yuanbo Guo, Zhengxiang Pan, and Jeff Heflin. 2005. LUBM: A benchmark for OWL knowledge base systems. Web Semant.: Sci., Serv. Agents WWW 3, 2 (2005), 158--182.

Digital Library

[31]

D. Harel, D. Kozen, and J. Tiuryn. 2000. Dynamic Logic. MIT Press.

Digital Library

[32]

S. Harris and A. Seaborne. 2013. SPARQL 1.1 Query Language. W3C Recommendation. Retrieved from http://www.w3.org/TR/sparql11-query.

[33]

Daniel Hernández, Aidan Hogan, Cristian Riveros, Carlos Rojas, and Enzo Zerega. 2016. Querying wikidata: Comparing SPARQL, relational and graph databases. In Proceedings of the International Semantic Web Conference (ISWC’16). 88--103.

[34]

Shan Shan Huang, Todd Jeffrey Green, and Boon Thau Loo. 2011. Datalog and emerging applications: An interactive tutorial. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. ACM, 1213--1216.

Digital Library

[35]

Egor V. Kostylev, Juan L. Reutter, Miguel Romero, and Domagoj Vrgoč. 2015. SPARQL with property paths. In Proceedings of the International Semantic Web Conference (ISWC’15). 3--18.

Digital Library

[36]

L. Libkin. 2004. Elements of Finite Model Theory. Springer.

Digital Library

[37]

L. Libkin, W. Martens, and D. Vrgoč. 2013. Querying graph databases with XPath. In Proceedings of the International Conference on Database Theory (ICDT’13).

Digital Library

[38]

Leonid Libkin, Wim Martens, and Domagoj Vrgoč. 2016. Querying graphs with data. J. ACM 63, 2 (2016), 14.

Digital Library

[39]

Leonid Libkin, Juan L. Reutter, and Domagoj Vrgoč. 2013. Trial for RDF: Adapting graph query languages for RDF data. In Proceedings of the Symposium on Principles of Database Systems (PODS’13). 201--212.

Digital Library

[40]

Christopher Longyear. 1972. Towards a triadic calculus, I-III. J. Cybernet. (1972), 50--65.

[41]

K. Losemann and W. Martens. 2012. The complexity of evaluating path expressions in SPARQL. In Proceedings of the Symposium on Principles of Database Systems (PODS’12). 101--112.

Digital Library

[42]

M. Marx. 2005. Conditional XPath. ACM Trans. Database Syst. 30, 4 (2005), 929--959.

Digital Library

[43]

Neo4j. 2013. Neo4j, The graph database. Retrieved from http://www.neo4j.org/.

[44]

Charles Sanders Peirce. 1885. On the algebra of logic: A contribution to the philosophy of notation. Amer. J. Math. 7, 2 (1885), 180--196.

[45]

Jorge Pérez, Marcelo Arenas, and Claudio Gutierrez. 2009. Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34, 3 (2009), 16.

Digital Library

[46]

J. Pérez, M. Arenas, and C. Gutierrez. 2010. nSPARQL: A navigational language for RDF. J. Web Semant. 8, 4 (2010), 255--270.

Digital Library

[47]

Axel Polleres and Johannes Peter Wallner. 2013. On the relation between SPARQL1.1 and answer set programming. J. Appl. Non-Class. Logics 23, 1--2 (2013), 159--212.

[48]

Juan L. Reutter, Miguel Romero, and Moshe Y. Vardi. 2015. Regular queries on graph databases. In Proceedings of the 18th International Conference on Database Theory (ICDT’15), Vol. 31. 177--194.

[49]

Juan L. Reutter, Adrián Soto, and Domagoj Vrgoč. 2015. Recursion in SPARQL. In Proceedings of the Semantic Web Conference (ISWC’15). 19--35.

Digital Library

[50]

Edward L. Robertson. 2004. Triadic relations: An algebra for the semantic web. In Proceedings of the Semantic Web and Databases 2nd International Workshop (SWDB’04). 91--108.

Digital Library

[51]

S. Rudolph and M. Krötzsch. 2013. Flag 8 check: Data access with monadically defined queries. In Proceedings of the Symposium on Principles of Database Systems (PODS’13). 151--162.

Digital Library

[52]

E. Schröder. 1890. 1905: Vorlesungen über die Algebra der Logik. 3 Bde. (1890).

[53]

Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). 697--706.

Digital Library

[54]

A. Tarski and S. Givant. 1987. A Formalization of Set Theory Without Variables. AMS.

[55]

M. Y. Vardi. 1995. On the complexity of bounded-variable queries. In Proceedings of the Symposium on Principles of Database Systems (PODS’95). 266--276.

Digital Library

[56]

D. Vrgoč. 2014. Querying Graphs with Data. Ph.D. Dissertation. School of Informatics, University of Edinburgh.

[57]

P. T. Wood. 2012. Query languages for graph databases. Sigmod Rec. 41, 1 (2012), 50--60.

Digital Library

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De Leo VFenu GGreco DBidotti NPlatter PMotta ENuzzolese AOsborne FRecupero D(2023)Ontology-Based Generation of Data Platform Assets2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386910(2937-2941)Online publication date: 15-Dec-2023
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Pacaci ABonifati AOzsu M(2022)Evaluating Complex Queries on Streaming Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00025(272-285)Online publication date: May-2022
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Tang XWu NPan Y(2022)An Algebra Supporting Semantic Query for Graph Data Model2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)10.1109/ICCECE54139.2022.9712796(838-841)Online publication date: 14-Jan-2022
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Index Terms

TriAL: A Navigational Algebra for RDF Triplestores
1. Information systems
  1. Data management systems
    1. Query languages
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

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Published In

cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 43, Issue 1

Best of SIGMOD 2016 Papers and Regular Papers

March 2018

227 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/3194314

Editor:
Christian S. Jensen
Aalborg University, Denmark

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Copyright © 2018 ACM.

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Publication History

Published: 23 March 2018

Accepted: 01 September 2017

Revised: 01 July 2017

Received: 01 November 2016

Published in TODS Volume 43, Issue 1

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https://doi.org/10.1109/BigData59044.2023.10386910
Pacaci ABonifati AOzsu M(2022)Evaluating Complex Queries on Streaming Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00025(272-285)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00025
Tang XWu NPan Y(2022)An Algebra Supporting Semantic Query for Graph Data Model2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)10.1109/ICCECE54139.2022.9712796(838-841)Online publication date: 14-Jan-2022
https://doi.org/10.1109/ICCECE54139.2022.9712796
Li GLi W(2022)Research on storage method for fuzzy RDF graph based on Neo4jEvolutionary Intelligence10.1007/s12065-022-00715-017:1(429-439)Online publication date: 8-Apr-2022
https://doi.org/10.1007/s12065-022-00715-0
Ma ZLi GMa RMa ZLi GMa R(2022)Persistence of Fuzzy RDF and Fuzzy RDF SchemaModeling and Management of Fuzzy Semantic RDF Data10.1007/978-3-031-11669-8_4(109-150)Online publication date: 9-Sep-2022
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Reutter JSoto AVrgoč D(2021)Recursion in SPARQLSemantic Web10.3233/SW-20040112:5(711-740)Online publication date: 1-Jan-2021
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Sharma CSinha RJohnson K(2021)Practical and comprehensive formalisms for modelling contemporary graph query languagesInformation Systems10.1016/j.is.2021.101816102:COnline publication date: 1-Dec-2021
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