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A Temporal RDF Model for Multi-grained Time Information Modeling

Published: 28 September 2021 Publication History

Abstract

With the rapid increase of temporal data, how to represent and manage temporal data has become a research issue worth digging in. To better represent temporal data, there have been many works on adding the dimension to RDF or other data representations such as relational databases. However, few works pay attention to the problem of updating time information in the form of triple elements in RDF. Note that this not only makes it easy to express that the relationship between entities is effective over a period of time, but also makes it easy to express that the entities themselves are effective in the time. A model of temporal data representation based on RDF is proposed in this paper which not only considering the validity of triples, but also considering the temporal validity of the entities themselves within the triples.

References

[1]
Frank Manola, Eric Miller, W3C.RDF Primer (W3C Recommendation 10 February 2004). https://www.w3.org/TR/2004/REC-rdf-primer-20040210/
[2]
Rodriguez, Alejandro, "Semantic management of streaming data." Proceedings of the 2nd International Conference on Semantic Sensor Networks-Volume 522. CEUR-WS. org, 2009.
[3]
Zhang, Fu, "Temporal Data Representation and Querying Based on RDF." IEEE Access 7 (2019): 85000-85023.
[4]
McBride, Brian, and Mark Butler. "Representing and querying historical information in RDF with application to E-discovery." HP Laboratories Technical Report, HPL-2009-261 (2009).
[5]
Zimmermann, Antoine, "A general framework for representing, reasoning and querying with annotated semantic web data." Journal of Web Semantics 11 (2012): 72-95.
[6]
Dylla, Maximilian, Mauro Sozio, and Martin Theobald. "Resolving temporal conflicts in inconsistent rdf knowledge bases". Datenbanksysteme für Business, Technologie und Web (BTW) (2011).
[7]
Tansel, A.U. : Temporal Databases: Theory, Design and Implementation, Benjamin/Cummings, 1993.
[8]
Snodgrass, R.T.: The TSQL2 Temporal Query Language. Kluwer, 1995.
[9]
Kulkarni, K.G., Michels, J.-E.: Temporal features in SQL:2011. SIGMOD Rec., 41 (3): 34-43, 2012.
[10]
Zemke, F.: What's new in SQL:2011. SIGMOD Rec., 41 (1): 67-73, 2012.
[11]
Jensen, C.S. : The consensus glossary of temporal database concepts. In Temporal Databases, Dagstuhl, pp. 367-405 (1997)
[12]
Gutierrez, Claudio, Carlos Hurtado and Alejandro Vaisman. Temporal rdf. European Semantic Web Conference. Springer, Berlin, Heidelberg, 2005.
[13]
Gutierrez, Claudio, Carlos A. Hurtdo and Alejandro Vaisman. Introducing time into RDF. IEEE Transactions on Knowledge and Data Engineering, 19 (2): 207-218, 2006.
[14]
Pugliese, Andrea, Octavian Udrea and V. S. Subrahmanian. Scaling RDF with time. Proceedings of the 17th International Conference on World Wide Web. pp. 605-614, ACM, 2008.
[15]
Motik, Boris. "Representing and querying validity time in RDF and OWL: A logic-based approach." Journal of Web Semantics,12 (2012): 3-21.
[16]
Grandi, Fabio. "Multi-temporal RDF Ontology Versioning." IWOD@ ISWC. 2009.
[17]
Udrea, Octavian, Diego Reforgiato Recupero, and V. S. Subrahmanian. "Annotated rdf." ACM Transactions on Computational Logic (TOCL) 11.2 (2010): 1-41.
[18]
Hoffart, Johannes, "YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia." Artificial Intelligence 194 (2013): 28-61.
[19]
Analyti, Anastasia, and Ioannis Pachoulakis. "A survey on models and query languages for temporally annotated RDF." International Journal of Advanced Computer Science and Applications 3.9 (2012): 28-35.
[20]
Rodriguez, Alejandro, "Semantic management of streaming data." Proceedings of the 2nd International Conference on Semantic Sensor Networks-Volume 522. CEUR-WS. org, 2009.
[21]
Steve Harris, Garlik, Andy Seaborne. SPARQL 1.1 Query Language (W3C Recommendation 21 March 2013). https://www.w3.org/TR/sparql11-query/
[22]
Patrick Hayes, Brian McBride. RDF Semantics (W3C Recommendation 10 February 2004). https://www.w3.org/TR/2004/REC-rdf-mt-20040210/
[23]
McBride, Brian, and Mark Butler. "Representing and querying historical information in RDF with application to E-discovery." HP Laboratories Technical Report, HPL-2009-261 (2009).
[24]
Gergatsoulis, Manolis and Pantelis Lilis. Multidimensional rdf. OTM Confederated International Conferences on the Move to Meaningful Internet systems. Springer, Berlin, Heidelberg, pp. 1188-1205, 2005.
[25]
Carlos Buil Aranda, SPARQL 1.1 Overview (W3C Recommendation 21 March 2013). http://www.w3.org/TR/sparql11-overview/
[26]
Bykau, Siarhei On modeling and querying concept evolution. Journal on Data Semantics, 1 (1): 31-55, 2012.
[27]
Bereta, Konstantina, Panayiotis Smeros, and Manolis Koubarakis. "Representation and querying of valid time of triples in linked geospatial data." Extended Semantic Web Conference. Springer, Berlin, Heidelberg, 2013.
[28]
Fabien Gandon, Guus Schreiber. RDF 1.1 XML Syntax (W3C Recommendation 25 February 2014). https://www.w3.org/TR/rdf-syntax-grammar/
[29]
Hurtado, Carlos, and Alejandro Vaisman "Reasoning with temporal constraints in RDF".International Workshop on Principles and Practice of Semantic Web Reasoning. Springer, Berlin, Heidelberg, 2006.
[30]
T Berners-Lee, J Hendler, O Lassila. The semantic web. New York: Scientific American, 2001,284(5): 34∼43

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cover image ACM Other conferences
DSIT 2021: 2021 4th International Conference on Data Science and Information Technology
July 2021
481 pages
ISBN:9781450390248
DOI:10.1145/3478905
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 September 2021

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Author Tags

  1. RDF
  2. Temporal data
  3. modeling

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  • Research-article
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  • Refereed limited

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  • the Basic Research Program of Jiangsu Province

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DSIT 2021

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Overall Acceptance Rate 114 of 277 submissions, 41%

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