Lightweight Semantics over Web Information Systems Content Employing Knowledge Tags

  • Mária Bieliková
  • Karol Rástočný
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7518)

Abstract

A model of web information system content is crucial for its effective manipulation. We employ knowledge tags – metadata that describe an aspect of an information artifact for purpose of the modeling. Knowledge tags provide a lightweight semantics over the content, which serves web information systems also for sharing knowledge about the content and interconnections between information artifacts. Knowledge tags represent not only content based metadata, but also a collaboration metadata, e.g. aggregations of an implicit user feedback including interactions with the content. To allow this type of metadata we should provide means for knowledge tags repository providing flexible and fast access for effective reasoning. In this paper we address issues related to knowledge tags repository and its automatic maintenance. Main design issues are based on considering dynamic character of the web of information artifacts, which results in content changes in time that can invalidate knowledge tags. We realized the web-scale repository via the MongoDB database. Proposed repository stores knowledge tags in Open Annotation model and supports inference via distributed SPARQL query processing algorithm for MapReduce.

Keywords

lightweight semantics knowledge tag annotation maintenance MapReduce SPARQL distributed repository 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bieliková, M., Barla, M., Šimko, M.: Lightweight Semantics for the “Wild Web”. In: IADIS Int. Conf. WWW/Internet 2011. IADIS Press (2011)Google Scholar
  2. 2.
    Bieliková, M., et al.: Collaborative Programming: The Way to “Better” Software. In: 6th Workshop on Int. and Knowledge Oriented Tech., Košice, pp. 89–94 (2011) (in Slovak)Google Scholar
  3. 3.
    Šimko, M.: Automated Acquisition of Domain Model for Adaptive Collaborative Web-Based Learning. Inf. Sciences and Tech., Bulletin of the ACM Slovakia 2(4), 9 p. (2012)Google Scholar
  4. 4.
    Priest, R., Plimmer, B.: RCA: Experiences with an IDE Annotation Tool. In: 6th ACM SIGCHI New Zealand Chapter’s Int. Conf. on Computer-human Interaction Design Centered HCI, pp. 53–60. ACM Press, New York (2006)CrossRefGoogle Scholar
  5. 5.
    Phelps, T.A., Wilensky, R.: Robust Intra-Document Locations. Computer Networks 33, 105–118 (2000)CrossRefGoogle Scholar
  6. 6.
    Plimmer, B., et al.: iAnnotate: Exploring Multi-User Ink Annotation in Web Browsers. In: 9th Australasian Conf. on User Interface, pp. 52–60. Australian Comp. Soc. (2010)Google Scholar
  7. 7.
    Kahan, J., Koivunen, M.R.: Annotea: An Open RDF Infrastructure for Shared Web Annotations. In: 10th Int. Conf. on WWW, pp. 623–632. ACM Press, New York (2001)CrossRefGoogle Scholar
  8. 8.
    Sanderson, R., Van de Sompel, H.: Making Web Annotations Persistent over Time. In: 10th Annual Joint Conf. on Digit. Lib., pp. 1–10. ACM Press, New York (2010)Google Scholar
  9. 9.
    Van de Sompel, H., Nelson, M.L., Sanderson, R., Balakireva, L.L., Ainsworth, S., Shankar, H.: Memento: Time Travel for the Web. CoRR. abs/0911.1, p. 14 (2009)Google Scholar
  10. 10.
    Gerber, A., Hyland, A., Hunter, J.: A Collaborative Scholarly Annotation System for Dynamic Web Documents – A Literary Case Study. In: Chowdhury, G., Koo, C., Hunter, J. (eds.) ICADL 2010. LNCS, vol. 6102, pp. 29–39. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Yu, C.H., Groza, T., Hunter, J.: High Speed Capture, Retrieval and Rendering of Segment-Based Annotations on 3D Museum Objects. In: Xing, C., Crestani, F., Rauber, A. (eds.) ICADL 2011. LNCS, vol. 7008, pp. 5–15. Springer, Heidelberg (2011)Google Scholar
  12. 12.
    Rohloff, K., Dean, M., Emmons, I., Ryder, D., Sumner, J.: An Evaluation of Triple-Store Technologies for Large Data Stores. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 1105–1114. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Tiwari, S.: Professional NoSQL. John Wiley & Sons, Inc., Indianapolis (2011)Google Scholar
  14. 14.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  15. 15.
    Kim, H.S., Ravindra, P., Anyanwu, K.: From SPARQL to MapReduce: The Journey Using a Nested TripleGroup Algebra. VLDB Endowment 4, 1426–1429 (2011)Google Scholar
  16. 16.
    Myung, J., Yeon, J., Lee, S.: SPARQL Basic Graph Pattern Processing with Iterative MapReduce. In: Workshop on Massive Data Analytics on the Cloud, p. 6. ACM Press, New York (2010)Google Scholar
  17. 17.
    Kuric, E.: Automatic Photo Annotation Based on Visual Content Analysis. Information Sciences and Technologies. Bulletin of the ACM Slovakia 2(3), 72–75 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mária Bieliková
    • 1
  • Karol Rástočný
    • 1
  1. 1.Institute of Informatics and Software Engineering, Faculty of Informatics and Information TechnologiesSlovak University of TechnologyBratislavaSlovakia

Personalised recommendations