Skip to main content
SpringerLink
Log in

Finding semantic associations in hierarchically structured groups of Web data

  • Original Article
  • Published:
Formal Aspects of Computing

Abstract

Most of the activities usually performed by Web users are today effectively supported by using appropriate metadata that make the Web practically readable by software agents operating as users’ assistants. While the original use of metadata mostly focused on improving queries on Web knowledge bases, as in the case of SPARQL-based applications on RDF data, other approaches have been proposed to exploit the semantic information contained in metadata for performing more sophisticated knowledge discovery tasks. Finding semantic associations between Web data seems a promising framework in this context, since it allows that novel, potentially interesting information can emerge by the Web’s sea, deeply exploiting the semantic relationships represented by metadata. However, the approaches for finding semantic associations proposed in the past do not seem to consider how Web entities are logically collected into groups, that often have a complex hierarchical structure. In this paper, we focus on the importance of taking into account this additional information, and we propose an approach for finding semantic associations which would not emerge without considering the structure of the data groups. Our approach is based on the introduction of a new metadata model, that is an extension of the direct, labelled graph allowing the possibility to have nodes with a hierarchical structure. To evaluate our approach, we have implemented it on the top of an existing recommender system for Web users, experimentally analyzing the introduced advantages in terms of effectiveness of the recommendation activity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Aleman-Meza B, Nagarajan M, Ding L, Sheth AP, Arpinar IP, Joshi A, Finin T (2008) Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection. ACM Trans Web 2(1): 1–29

    Article  Google Scholar 

  2. Anyanwu K, Maduko A, Sheth A (2005) Semrank: ranking complex relationship search results on the semantic web. In: WWW ’05: Proceedings of the 14th international conference on World Wide Web. ACM, New York, pp 117–127

  3. Anyanwu K, Sheth A (2002) The ρ operator: discovering and ranking associations on the semantic Web. SIGMOD Rec 31(4): 42–47

    Article  Google Scholar 

  4. Anyanwu K, Sheth A (2003) P-queries: enabling querying for semantic associations on the semantic Web. In: WWW ’03: Proceedings of the 12th international conference on World Wide Web. ACM, New York, pp 690–699

  5. Barton S (2004) Designing indexing structure for discovering relationships in rdf graphs. In: Proceedings of the Dateso 2004 Annual International Workshop on DAtabases, TExts, Specifications and Objects, Desna, Czech Republic, April 14–16, 2004, volume 98 of CEUR Workshop Proceedings. CEUR-WS.org, pp 7–17.

  6. Garruzzo S, Rosaci D (2008) Agent clustering based on semantic negotiation. ACM Trans Auton Adapt Syst 3(2): 1–40

    Article  Google Scholar 

  7. Getoor L, Diehl CP (2005) Link mining: a survey. SIGKDD Explor. Newsl 7(2): 3–12

    Article  Google Scholar 

  8. Groppe J, Groppe S, Ebers S, Linnemann V (2009) Efficient processing of sparql joins in memory by dynamically restricting triple patterns. In: SAC ’09: proceedings of the 2009 ACM symposium on applied computing. ACM, New York, pp 1231–1238

  9. Hjel J (2001) Creating the semantic web with RD. Wiley, New York

  10. Kamei K, Yoshida S, Kuwabara K, Akahani J, Satoh T (2003) An agent framework for inter-personal information sharing with an rdf-based repository. In: Proc. of the Semantic Web—ISWC 2003, Second International Semantic Web Conference, Sanibel Island, FL, USA, volume 2870 of Lecture Notes in Computer Science. Springer, pp 438–452

  11. Kim D, Atluri V, Bieber M, Adam N, Yesha Y (2004) A clickstream-based collaborative filtering personalization model: towards a better performance. In: Proceedings of the int. workshop on web information and data management. ACM, pp 88–95

  12. Kochut K, Janik M (2007) Sparqler: extended sparql for semantic association discovery. In: The semantic web: research and applications, 4th european semantic web conference, ESWC 2007, Innsbruck, Austria, volume 4519 of Lecture Notes in Computer Science. Springer, pp 145–159

  13. MUADDIB Project URL (2009). http://www.muad.altervista.org.

  14. Ramakrishnan C, Milnor WH, Perry M, Sheth AP (2005) Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explor. Newsl 7(2): 56–63

    Article  Google Scholar 

  15. RDF W3C URL (2013). http://www.w3.org/rdf.

  16. Rosaci D, Sarné GML (2006) Masha: a multi-agent system handling user and device adaptivity of web sites. User Model User Adapted Interact 16(5): 435–462

    Article  Google Scholar 

  17. Rosaci D, Sarné GML, Garruzzo S (2009) MUADDIB: a distributed recommender system supporting device adaptivity. ACM Trans Inf Syst 27(4): 1–41

    Article  Google Scholar 

  18. Schenk S, Staab S (2008) Networked graphs: a declarative mechanism for sparql rules, sparql views and rdf data integration on the web. In: WWW ’08: Proceeding of the 17th international conference on World Wide Web. ACM, pp 585–594

  19. Semantic Web W3C URL (2009). http://www.w3.org/2001/2w.

  20. Stocker M, Seaborne A, Bernstein A, Kiefer C, Reynolds D (2008) Sparql basic graph pattern optimization using selectivity estimation. In: WWW ’08: Proceeding of the 17th international conference on World Wide Web. ACM, New York, pp 595–604

  21. Theoharis Y, Tzitzikas Y, Kotzinos D, Christophides V (2008) On graph features of semantic web schemas. IEEE Trans Knowl Data Eng 20(5): 692–702

    Article  Google Scholar 

  22. Tong H, Faloutsos C (2006) Center-piece subgraphs: problem definition and fast solutions. In: KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 404–413

  23. Wang W, Barnaghi PM, Bargiela A (2008) Search with meanings: an overview of semantic search systems. Int J Commun SIWN 3: 76–82

    Google Scholar 

  24. Weikum G (2009) Harvesting, searching, and ranking knowledge on the web: invited talk. In: WSDM ’09: proceedings of the second acm international conference on web search and data mining. ACM, New York, pp 3–4

  25. Zhuge H (2009) Communities and emerging semantics in semantic link network: discovery and learning. IEEE Trans Knowl Data Eng 21(6): 785–799

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIIES Department, University of Reggio Calabria, Via Graziella Loc, Feo di Vito, 89122, Reggio Calabria, Italy

    Domenico Rosaci

Authors
  1. Domenico Rosaci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Domenico Rosaci.

Additional information

Jim Woodcock

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rosaci, D. Finding semantic associations in hierarchically structured groups of Web data. Form Asp Comp 27, 867–884 (2015). https://doi.org/10.1007/s00165-015-0337-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00165-015-0337-z

Keywords

Search

Navigation