Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 167-176.doi: 10.11896/jsjkx.200900114

• Database & Big Data & Data Science • Previous Articles     Next Articles

Temporal RDF Model and Index Method Based on Neighborhood Structure

CHEN Yuan-yuan, YAN Li, ZHANG Zhe-qing, MA Zong-min   

  1. College of Computer Science and Technology/College of Artificial Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2020-09-14 Revised:2020-12-31 Online:2021-10-15 Published:2021-10-18
  • About author:CHEN Yuan-yuan,born in 1996,postgraduate.Her main research interests include RDF data and the semantic web.
    MA Zong-min,born in 1965,Ph.D,professor.His main research interests include databa-ses,the semantic web,knowledge representation andreaso-ning,information uncertainty.

PDF (PC)

637

Abstract: Resource description framework (RDF) is a metadata model and information description specification recommended by W3C,which is widely used in various fields.To track changes in RDF data over time,temporal information is introduced into the RDF framework.With the rapid growth of temporal RDF data,effective management of temporal RDF data is necessary.A reasonable index mechanism can achieve efficient storage and query of data.In this paper,we first present a temporal RDF data mo-del.We propose a specific one-dimensional coding scheme,which represent temporal data simply and extend the existing RDF data model with lower overhead.Furthermore,we present its two levels of indexes based on neighborhood structure.The first one uses dynamic counting filter to index the neighborhood information of the node,and the second builds the B+ tree to index the temporal RDF data related to each node.Moreover,large-scale temporal RDF data can be updated.Experimental results show that the proposed method is around 35% better than the comparison method in most cases,and it is scalable and effective.

Key words: Dynamic counting filter, Encoding, Index structure, RDF, Temporal RDF

CLC Number: 

  • TP399
[1]BERNERS-LEE T,HENDLER J,LASSILA O.The semanticWeb[J].Scientific American,2001,284(5):28-37.
[2]World Wide Web Consortium:RDF/XML Syntax Specification (Revised)[EB/OL].[2020-05-30].http://www.w3.org/TR/2004/REC-rdf-syntax-grammer.
[3]AUER S,BIZER C,KOBILAROV G,et al.DBpedia:A nucleus for a Web of open data[J].The Semantic Web,2007,4825:722-735.
[4]WISHART D S,KNOX C,GUO A C,et al.DrugBank:a comprehensive resource for in silico drug discovery and exploration[J].Nucleic Acids Research,2006,34:668-672.
[5]WICK M.GeoNames [EB/OL].[2020-05-30].https://www.geonames.org/.
[6]Technology Transformation Service.Data.gov [EB/OL].[2020-05-30].https://www.data.gov/.
[7]W3C SWEO Community Project.Linking open data on the semantic Web [EB/OL].[2020-05-30].https://lod-cloud.net/.
[8]CARROLL J,DICKINSON I,DOLLIN C,et al.Jena:Implementing the semantic Web recommendations[C]//Proceeding of the World Wide Web Conference on Alternate Track Papers and Posters.New York:ACM,2004:74-83.
[9]NEUMANN T,WEIKUM G.The RDF-3X engine for scalable management of RDF data[J].The VLDB Journal,2010,19(1):91-113.
[10]MADDURI K,WU K.Massive-scale RDF processing usingcompressed bitmap indexes[C]//LNCS 6809:Statistical and Scientific Database Management.Berlin:Springer,2011:470-479.
[11]JANIK M,KOCHUT K.BRAHMS:A WorkBench RDF store and high performance memory system for semantic association discovery[C]//Proceeding of the International Semantic Web Conference.Berlin:Springer,2005:431-445.
[12]UDREA O,PUGLIESE A,SUBRAHMANIAN V.Grin:Agraph based RDF index[C]//Proceeding of the National Confe-rence on Artificial Intelligence.Palo Alto:AAAI Press,2007:1465-1470.
[13]KIM K,MOON B,KIM H J.RG-index:An RDF graph index for efficient SPARQL query processing[J].Expert Systems with Applications,2014,41(10):4596-4607.
[14]JENSEN C.The consensus glossary of temporal database concepts－February 1998 version[C]//LNCS 1399:Temporal Databases:Research and Practice.Berlin:Springer,1997:367-405.
[15]KRISHNA G,MICHELS J.Temporal features in SQL:2011[J].SIGMOD Record,2012,41(3):34-43.
[16]ZAIDI A K.A temporal programmer for time-sensitive modeling of discrete event systems[C]//Systems Man and Cybernetics.Piscataway,NJ:IEEE,2000:2186-2191.
[17]PEUQUET D.Making space for time:Issues in space-time data representation[J].GeoInformatica,2001,5:11-32.
[18]NORVAG K,NYBO A O.DyST:Dynamic and Scalable Temporal Text Indexing[C]//International Symposium on Temporal Representation and Reasoning.Piscataway,NJ:IEEE,2006:204-211.
[19]CLAUDIO G,CARLOS A H,ALEJANDRO A.Temporal RDF[C]//LNCS 3532:The semantic Web:Research and Applications.Berlin:Springer,2005:93-107.
[20]GUTIERREZ C,HUIRADO C A,VAISMAN A.Introducing time into RDF[J].IEEE Transactions on Knowledge and Data Engineering,2006,19(2):207-218.
[21]GRANDI F.Multi-temporal RDF ontology versioning[J].CEUR Workshop Proceedings,2009,519:1-10.
[22]ZHANG F,WANG K,LI Z Y,et al.Temporal data representation and querying based on RDF[J].IEEE Access,2019,7:85000-85023.
[23]MOTIK B.Representing and querying validity time in RDF and OWL:A logic-based approach[J].Journal of Web Semantics,2012,12:3-21.
[24]PUGLIESE A,UDREA O,SUBRAHMANIAN V S.ScalingRDF with time[C]//Proceeding of the 17th Int Conference on World Wide Web.New York:ACM,2008:605-614.
[25]TAPPOLET J,BERNSTEIN A.Applied temporal RDF-efficient temporal querying of RDF data with SPARQL[C]//LNCS 5554:The Semantic Web:Research and Applications.Berlin:Springer,2009:308-322.
[26]YAN L,ZHAO P,MA Z M.Indexing temporal RDF graph[J].Computing,2019,101(10):1457-1488.
[27]ZHAO P,YAN L.A methodology for indexing temporal RDF data[J].Journal of Information Science and Engineering,2019,35(4):923-934.
[28]WANG Y F,ZHU M J,QU L Z,et al.Timely YAGO:Harvesting,querying,and visualizing temporal knowledge from Wikipedia[C]//Proceeding of the 13th International Conference on Extending Database Technology.New York:ACM,2010:697-700.
[29]KOUBARAKIS M,KYZIRAKOS K.Modeling and queryingmetadata in the semantic sensor Web:The model stRDF and the query language stSPARQL[C]//LNCS 6088:Proceeding of the 7th Extended Semantic Web Conference.Berlin:Springer,2010:425-439.
[30]ZIMMERMANN A,LOPES N,POLLERES A,et al.A general framework for representing,reasoning and querying with annotated semantic Web data[J].Journal of Web Semantics,2012,11:72-95.
[31]LIAGOURIS J,MAMOULIS N,BOUROS P,et al.An effective encoding scheme for spatial RDF data[J].Proceedings of the VLDB Endowment,2014,7(12):1271-1282.
[32]VLACHOU A,DOULKERIDIS C,GLENIS A,et al.Efficientspatio-temporal RDF query processing in large dynamic know-ledge bases[C]//ACM Symp on Applied Computing.New York:ACM,2019:439-447.
[33]CYGANIAK R,HARTH A,HOGAN A.N-Quads:Enxtending N-Triples with Context [EB/OL].[2020-05-30].http://sw.deri.org/2008/07/n-quads/.
[34]TIAN Y Y,JIGNESH M.TALE:A tool for approximate large graph matching[C]//Proceeding of the 2008 IEEE 24th International Conference on Data Engineering.Los Alamitos,CA:IEEE Computer Society,2008:963-972.
[35]KHAN A,LI N,YAN X,et al.Neighborhood based fast graph search in large networks[C]//Proceeding of the 2011 ACM SIGMOD International Conference on Management of Data.New York:ACM,2011:901-912.
[36]ALUÇ G,HARTIG O,ÖZSU T,et al.Diversified stress testing of RDF data management systems[C]//LNCS 8796:Int. Semantic Web Conf.Berlin:Springer,2014:197-212.
[37]AGUILAR-SABORIT J,TRANCOSO P,MUNTES-MULERO V,et al.Dynamic count filters[J].ACM SIGMOD Record,2006,35(1):26-32.
[1] QU Zhong, CHEN Wen. Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion [J]. Computer Science, 2022, 49(3): 192-196.
[2] TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[3] SHI Heng, ZHANG Ling. Image Shadow Removal Algorithm Based on Generative Adversarial Network [J]. Computer Science, 2021, 48(6): 145-152.
[4] WANG Yin-di, ZHANG Zhe-qing, YAN Li. Indexing Bi-temporal RDF Model [J]. Computer Science, 2021, 48(4): 63-69.
[5] LU Jia-wen, YAN Li. Mapping Method from Object-relational Database to RDF(S) [J]. Computer Science, 2021, 48(10): 145-151.
[6] QIAO Bo-wen,LI Jun-hui. Neural Machine Translation Combining Source Semantic Roles [J]. Computer Science, 2020, 47(2): 163-168.
[7] JI Ming-xuan, SONG Yu-rong. New Machine Translation Model Based on Logarithmic Position Representation and Self-attention [J]. Computer Science, 2020, 47(11A): 86-91.
[8] ZHANG Jing-jing, ZHANG Ai-hua, JI Hai-feng. Image Compression Encoding Based on Wavelet Transform and Fractal [J]. Computer Science, 2019, 46(8): 310-314.
[9] ZHOU Yi-hua, LI Guang-hui, YANG Yu-guang, SHI Wei-min. Location Privacy Preserving Nearest Neighbor Querying Based on GeoHash [J]. Computer Science, 2019, 46(8): 212-216.
[10] LU Hai-chuan, FU Hai-dong, LIU Yu. Geo-semantic Data Storage and Retrieval Mechanism Based on CAN [J]. Computer Science, 2019, 46(2): 171-177.
[11] YE Zhong-lin, ZHAO Hai-xing, ZHANG Ke, ZHU Yu. Network Representation Learning Based on Multi-view Ensemble Algorithm [J]. Computer Science, 2019, 46(1): 117-125.
[12] GONG Fa-ming,LI Xiao-ran. Research on Ontology Data Storage of Massive Oil Field Based on Neo4j [J]. Computer Science, 2018, 45(6A): 549-554.
[13] WANG Qian-ge, HE Pu, NIE Tie-zheng, SHEN De-rong, YU Ge. Survey of Data Storage and Query Techniques in Blockchain Systems [J]. Computer Science, 2018, 45(12): 12-18.
[14] YU Xin-yi, ZHAN Yi-an, ZHU Feng, OU Lin-lin. Improved ORB Feature Extraction Algorithm Based on Quadtree Encoding [J]. Computer Science, 2018, 45(11A): 222-225.
[15] YANG Kang, YUAN Hai-dong and GUO Yuan-bo. Research on Lower Redundancy of Two-dimension Barcode Encoding in Chinese Characters [J]. Computer Science, 2017, 44(Z11): 565-569.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.