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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2016: Artificial Intelligence and Soft Computing pp 333–343Cite as

Integration of Multiple Graph Datasets and Their Linguistic Summaries: An Application to Linked Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

Abstract

This paper presents a novel method of generating and evaluating linguistic summaries of content stored in distributed graph datasets, like LinkedData. Linguistic summarization is a well known data mining technique, aimed to discover patterns in data and present them in natural language. So far, this method has been researched only for relational databases. In our recent paper we have presented how to adapt this method for graph datasets. We have solved the problems of subject definition (further extended in this paper), retrieval of the attributes for summarization, generalization of summarizers and qualifiers. In this paper we extend that research by adapting proposed method to distributed interlinked graph datasets, which results in obtaining new summaries, and therefore new knowledge. We discuss how to follow different types of equivalence links that may exists between graph datasets. In order to measure characteristics specific for summaries of distributed graph data we propose new truth values (degree of subject appropriateness, degree of summarizer order and degree of linkage), and adapt existing ones (degree of covering). We run several experiments on Linked Data and discuss the results.

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References

  1. Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1:1–1:39 (2008)

    Article  Google Scholar 

  2. Hausenblas, M., Halb, W., Raimond, Y., Heath, T.: What is the size of the semantic web. In: Proceedings of the International Conference on Semantic Systems. ISemantics 2008 (2008)

    Google Scholar 

  3. Yager, R.R.: A new approach to the summarization of data. Inf. Sci. 28(1), 69–86 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kacprzyk, J., Yager, R.R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. Int. J. Appl. Math. Comput. Sci. 10(4), 813–834 (2000)

    MATH  Google Scholar 

  5. Kacprzyk, J., Wilbik, A., Zadrozny, S.: An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation. Int. J. Intell. Syst. 25(5), 411–439 (2010)

    MATH  Google Scholar 

  6. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. Newsl. 1(2), 12–23 (2000)

    Article  Google Scholar 

  7. Kosala, R., Blockeel, H.: Web mining research: a survey. SIGKDD Explor. Newsl. 2(1), 1–15 (2000)

    Article  Google Scholar 

  8. Stumme, G., Hotho, A., Berendt, B.: Semantic web mining: state of the art and future directions. Web Seman. Sci. Serv. Agents World Wide Web 4(2), 124–143 (2006). Semantic Grid - The Convergence of Technologies

    Article  Google Scholar 

  9. Aggarwal, C.C., Wang, H.: Managing and Mining Graph Data, 1st edn. Springer Publishing Company, Incorporated, US (2010)

    Book  MATH  Google Scholar 

  10. Cook, D.J., Holder, L.B.: Mining Graph Data. John Wiley & Sons, Hoboken (2006)

    Book  MATH  Google Scholar 

  11. Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the 2001 IEEE International Conference on Data Mining. ICDM 2001, Computer Society, pp. 313–320. IEEE, Washington, DC (2001)

    Google Scholar 

  12. Yan, X., Han, J.: gspan: graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining. ICDM 2002, Computer Society 721-724. IEEE, Washington, DC (2002)

    Google Scholar 

  13. Castelltort, A., Laurent, A.: Fuzzy queries over NoSQL graph databases: perspectives for extending the cypher language. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014, Part III. CCIS, vol. 444, pp. 384–395. Springer, Heidelberg (2014)

    Google Scholar 

  14. Strobin, L., Niewiadomski, A.: Linguistic summaries of graph datasets using ontologies: an application to semantic web. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawinski, B. (eds.) ICCCI 2015. LNCS, vol. 9329, pp. 380–389. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24069-5_36

    Chapter  Google Scholar 

  15. Castelltort, A., Laurent, A.: Extracting fuzzy summaries from nosql graph databases. In: Andreasen, T., Christiansen, H., Kacprzyk, J., Larsen, H., Pasi, G., Pivert, O., De Tré, G., Vila, M.A., Yazici, A., Zadrozny, S. (eds.) Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol. 400, pp. 189–200. Springer International Publishing, Switzerland (2016)

    Chapter  Google Scholar 

  16. Lehmann, J.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6(2), 167–195 (2014)

    Google Scholar 

  17. Cingolani, P., Alcalá-Fdez, J.: jfuzzylogic: a robust and flexible fuzzy-logic inference system language implementation. In: FUZZ-IEEE, pp. 1–8. IEEE (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, ul. Wólczańska 215, 90-924, Lodz, Poland

    Lukasz Strobin & Adam Niewiadomski

Authors
  1. Lukasz Strobin
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  2. Adam Niewiadomski
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Corresponding author

Correspondence to Lukasz Strobin .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2016 Springer International Publishing Switzerland

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Strobin, L., Niewiadomski, A. (2016). Integration of Multiple Graph Datasets and Their Linguistic Summaries: An Application to Linked Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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