3XL: Supporting efficient operations on very large OWL Lite triple-stores
An increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not u...
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| Vydáno v: | Information systems (Oxford) Ročník 36; číslo 4; s. 765 - 781 |
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01.06.2011
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| ISSN: | 0306-4379, 1873-6076 |
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| Abstract | An increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not using more advanced features such as logical inference, etc. However, current triple-stores are not optimized for such bulk operations and/or do not support OWL Lite. Further, triple-stores can be inflexible when the data has to be integrated with other kinds of data in non-triple form, e.g., standard relational data. This paper presents 3XL, a triple-store that efficiently supports operations on very large amounts of OWL Lite triples. 3XL also provides the user with high flexibility as it stores data in an object-relational database in a schema that is easy to use and understand. It is, thus, easy to integrate 3XL data with data from other sources. The distinguishing features of 3XL include (a) flexibility as the data is stored in a database, allowing easy integration with other data, and can be queried by means of both triple queries and SQL, (b) using a specialized data-dependent schema (with intelligent partitioning) which is intuitive and efficient to use, (c) using object-relational DBMS features such as inheritance, (d) efficient loading through extensive use of bulk loading and caching, and (e) efficient triple query operations, especially in the important case when the subject and/or predicate is known. Extensive experiments with a PostgreSQL-based implementation show that 3XL performs very well for such operations and that the performance is comparable to state-of-the-art triple-stores.
► A flexible and efficient OWL Lite triple-store. ► A specialized and data-dependent schema with intelligent partitioning. ► Use of object-relational features such as inheritance. |
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| AbstractList | An increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not using more advanced features such as logical inference, etc. However, current triple-stores are not optimized for such bulk operations and/or do not support OWL Lite. Further, triple-stores can be inflexible when the data has to be integrated with other kinds of data in non-triple form, e.g., standard relational data. This paper presents 3XL, a triple-store that efficiently supports operations on very large amounts of OWL Lite triples. 3XL also provides the user with high flexibility as it stores data in an object-relational database in a schema that is easy to use and understand. It is, thus, easy to integrate 3XL data with data from other sources. The distinguishing features of 3XL include (a) flexibility as the data is stored in a database, allowing easy integration with other data, and can be queried by means of both triple queries and SQL, (b) using a specialized data-dependent schema (with intelligent partitioning) which is intuitive and efficient to use, (c) using object-relational DBMS features such as inheritance, (d) efficient loading through extensive use of bulk loading and caching, and (e) efficient triple query operations, especially in the important case when the subject and/or predicate is known. Extensive experiments with a PostgreSQL-based implementation show that 3XL performs very well for such operations and that the performance is comparable to state-of-the-art triple-stores.
► A flexible and efficient OWL Lite triple-store. ► A specialized and data-dependent schema with intelligent partitioning. ► Use of object-relational features such as inheritance. An increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not using more advanced features such as logical inference, etc. However, current triple-stores are not optimized for such bulk operations and/or do not support OWL Lite. Further, triple-stores can be inflexible when the data has to be integrated with other kinds of data in non-triple form, e.g., standard relational data. This paper presents 3XL, a triple-store that efficiently supports operations on very large amounts of OWL Lite triples. 3XL also provides the user with high flexibility as it stores data in an object-relational database in a schema that is easy to use and understand. It is, thus, easy to integrate 3XL data with data from other sources. The distinguishing features of 3XL include (a) flexibility as the data is stored in a database, allowing easy integration with other data, and can be queried by means of both triple queries and SQL, (b) using a specialized data-dependent schema (with intelligent partitioning) which is intuitive and efficient to use, (c) using object-relational DBMS features such as inheritance, (d) efficient loading through extensive use of bulk loading and caching, and (e) efficient triple query operations, especially in the important case when the subject and/or predicate is known. Extensive experiments with a PostgreSQL-based implementation show that 3XL performs very well for such operations and that the performance is comparable to state-of-the-art triple-stores. A flexible and efficient OWL Lite triple-store. A specialized and data-dependent schema with intelligent partitioning. Use of object-relational features such as inheritance. Adapted from the source document. An increasing number of (semantic) web applications store a very large number of (subject, predicate, object) triples in specialized storage engines called triple-stores. Often, triple-stores are used mainly as plain data stores, i.e., for inserting and retrieving large amounts of triples, but not using more advanced features such as logical inference, etc. However, current triple-stores are not optimized for such bulk operations and/or do not support OWL Lite. Further, triple-stores can be inflexible when the data has to be integrated with other kinds of data in non-triple form, e.g., standard relational data. This paper presents 3XL, a triple-store that efficiently supports operations on very large amounts of OWL Lite triples. 3XL also provides the user with high flexibility as it stores data in an object-relational database in a schema that is easy to use and understand. It is, thus, easy to integrate 3XL data with data from other sources. The distinguishing features of 3XL include (a) flexibility as the data is stored in a database, allowing easy integration with other data, and can be queried by means of both triple queries and SQL, (b) using a specialized data-dependent schema (with intelligent partitioning) which is intuitive and efficient to use, (c) using object-relational DBMS features such as inheritance, (d) efficient loading through extensive use of bulk loading and caching, and (e) efficient triple query operations, especially in the important case when the subject and/or predicate is known. Extensive experiments with a PostgreSQL-based implementation show that 3XL performs very well for such operations and that the performance is comparable to state-of-the-art triple-stores. a-[ordm A flexible and efficient OWL Lite triple-store. a-[ordm A specialized and data-dependent schema with intelligent partitioning. a-[ordm Use of object-relational features such as inheritance. |
| Author | Liu, Xiufeng Thomsen, Christian Pedersen, Torben Bach |
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| Cites_doi | 10.1145/1559845.1559911 10.1007/3-540-48005-6_7 10.1145/1183512.1183522 10.1007/11581116_19 10.14778/1453856.1453927 10.21236/ADA451847 10.1007/11836025_42 10.14778/1454159.1454227 10.1016/j.websem.2005.06.005 10.1109/ICDE.2008.4497539 |
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| References_xml | – reference: S. Alexaki, V. Chrisophides, G. Karvounarakis, D. Plexousakis, On storing voluminous RDF descriptions: the case of web portal catalogs, in: Proceedings of WebDB, 2001, pp. 43–48. – reference: J. Dolby, A. Fokoue, A. Kalyanpur, A. Kershenbaum, E. Schonberg, K. Srinivas, L. Ma, Scalable semantic retrieval through summarization and refinement, in: Proceedings of AAAI, 2007, p. 299. – reference: S. Das, E. Chong, W. Zhe, M. Annamalai, J. Srinivasan, A scalable scheme for bulk loading large RDF graphs into oracle, in: Proceedings of ICDE, 2008, pp. 1297–1306. – reference: EIAO Ontology, Available at: – reference: S. Harris, N. Gibbins, 3Store: efficient bulk RDF storage, in: Proceedings of PSSS, 2003, pp. 1–15. – reference: as of 2010-10-18. – reference: S. Alexaki, V. Chrisophides, G. Karvounarakis, D. Plexousakis, K. Tolle, The ICS-FORTH RDFSuite: managing voluminous RDF description bases, in: Proceedings of ISWC, 2001, pp. 1–13. – reference: C. Thomsen, T.B. Pedersen, Building a web warehouse for accessibility data, in: Proceedings of DOLAP, 2006, pp. 43–50. – reference: BigOWLIM—Semantic Repository for RDF(S) and OWL, Available at: – reference: J. Broekstra, A. Kampman, F. van Harmelen, Sesame: a generic architecture for storing and querying RDF and RDF schema, in: Proceedings of ISWC, 2002, pp. 54–68. – reference: L. Sidirourgos, R. Goncalves, M. Kersten, N. Nes, S. Manegold, Column-store support for RDF data management: not all swans are white, in: Proceedings of the VLDB Endow, 2008, pp. 1553–1563. – reference: J. Zhou, L. Ma, Q. Liu, L. Zhang, Y. Yu, Y. Pan, Minerva: a scalable OWL ontology storage and inference system, in: Proceedings of ASWC, 2006, pp. 429–443. – reference: J.S.S. Narayanan, T. Kurc. DBOWL: Towards Extensional Queries on a Billion Statements Using Relational Databases, Technical Report, 2006, Available at: – year: 2004 ident: bib5 article-title: A Semantic Web Primer – reference: K. Wilkinson, C. Sayers, H. Kuno, D. Reynolds, Efficient RDF storage and retrieval in Jena2, in: Proceedings of SWDB, 2003, pp. 131–150. – reference: O. Lassila, R. Swick, Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation, 1999, Available at: – volume: 3 start-page: 158 year: 2005 end-page: 182 ident: bib4 article-title: LUBM: a benchmark for OWL knowledge base systems publication-title: J. Web Sem. – reference: G. Prud’ Hommeaux, A. Seaborne, et al. SPARQL query language for RDF, in: J. of W3C working draft, 2006. – reference: T. Neumann, G. Weikum, Scalable join processing on very large RDF graphs, in: Proceedings of SIGMOD, 2009, pp. 627–640. – reference: A. Kiryakov, D. Ognyanov, D. Manov. OWLIM—a pragmatic semantic repository for OWL, in: Proceedings of SSWS, 2005, pp. 182–192. – reference: Oracle Semantic Technologies Center, Available at: – reference: D.J. Abadi, A. Marcus, S.R. Madden, K. Hollenbach, Scalable semantic web data management using vertical partitioning, in: Proceedings of VLDB Endow, 2007, pp. 411–422. – reference: T. Neumann, G. Weikum, RDF-3X: a RISC-style engine for RDF, in: Proceedings of the VLDB Endow, 2008, pp. 647–659. – reference: P. Snyder, tmpfs: a virtual memory file system, in: Proceedings of EUUG, 1990, pp. 241–248. – reference: S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, D. McGuinness, P. Patel-Schneider, L. Stein, OWL Web Ontology Language Reference, W3C Recommendation, 2004, Available at: – reference: D.B. Berkeley, Oracle Embedded Database, Available at: – reference: Z. Pan, J. 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| Title | 3XL: Supporting efficient operations on very large OWL Lite triple-stores |
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