Explanation Retrieval in Semantic Networks - Understanding Spreading Activation based Recommendations

Vanessa N. Michalke, Kerstin Hartig

Abstract

Spreading Activation is a well-known semantic search technique to determine the relevance of nodes in a semantic network. When used for decision support, meaningful explanations of semantic search results are crucial for the user’s acceptance and trust. Usually, explanations are generated based on the original network. Indeed, the data accumulated during the spreading activation process contains semantically extremely valuable information. Therefore, our approach exploits the so-called spread graph, a specific data structure that comprises the spreading progress data. In this paper, we present a three-step explanation retrieval method based on spread graphs. We show how to retrieve the most relevant parts of a network by minimization and extraction techniques and formulate meaningful explanations. The evaluation of the approach is then performed with a prototypical decision support system for automotive safety analyses.

References

  1. Aleman-Meza, B., Halaschek-Weiner, C., Arpinar, I. B., Ramakrishnan, C., and Sheth, A. (2005). Ranking complex relationships on the semantic web. IEEE Internet Computing, 9(3):37-44.
  2. Alvarez, J. M., Polo, L., Jimenez, W., Abella, P., and Labra, J. E. (2011). Application of the spreading activation technique for recommending concepts of well-known ontologies in medical systems. Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB 7811), page 626.
  3. Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6):453-482.
  4. Crestani, F. and Lee, P. L. (2000). Searching the web by constrained spreading activation. Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval , 36(4):585-605.
  5. Faloutsos, C., McCurley, K. S., and Tomkins, A. (2004). Fast discovery of connection subgraphs. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 7804), pages 118-127, New York. ACM.
  6. Forcher, B., Agne, S., Dengel, A., Gillmann, M., and RothBerghofer, T. (2012). Towards Understandable Explanations for Document Analysis Systems. In 10th International Workshop on Document Analysis Systems(DAS) IAPR, pages 6-10, Gold Cost.
  7. Forcher, B., Roth-Berghofer, T., Sintek, M., and Dengel, A. (2010). Constructing Understandable Explanations for Semantic Search Results. In 17th International Conference, Knowledge Engineering and Management by the Masses (EKAW), pages 493-502, Lisbon. Springer.
  8. Forcher, B., Roth-Berghofer, T., Sintek, M., and Dengel, A. (2011). Semantic Logging: Towards ExplanationAware DAS. In International Conference on Document Analysis and Recognition (ICDAR'11), pages 1140-1144, Beijing. IEEE.
  9. Furnas, G. W. and Zacks, J. (1994). Multitrees: Enriching and Reusing Hierarchical Structure. In Conference on Human Factors in Computing Systems (CHI'94), pages 330-336, Bosten, Massachusetts. ACM.
  10. Hartig, K. and Karbe, T. (2016). Recommendationbased Decision Support for Hazard Analysis and Risk Assessment. In 8th International Conference on Information, Process, and Knowledge Management (eKNOW 7816), pages 108-111.
  11. Jain, S., Grover, A., Thakur, P. S., and Choudhary, S. K. (2015). Trends, Problems And Solutions of Recommender System. In International Conference on Computing, Communication and Automation (ICCCA'15), pages 955-958, Noida. IEEE.
  12. Kaklauskas, A. (2015). Intelligent Decision Support Systems. In Biometric and Intelligent Decision Making Support, volume 81, pages 195-220. Springer.
  13. Klahold, A. (2009). Empfehlungssysteme: Recommender Systems - Grundlagen, Konzepte und Lösungen [Recommender Systems - Fundamentals, concepts and solutions]. Vieweg + Teubner, Wiesbaden.
  14. RDF (2014). RDF 1.1 Concepts and Abstract Syntax. World Wide Web Consortium - W3C. Accessed: 15.06.2016.
  15. Sedgewick, R. and Wayne, K. (2011). Algorithms - Fourth Edition. Addison-Wesley, Boston, Massachusetts.
  16. Sinha, R. and Swearingen, K. (2002). The role of transparency in recommender systems. In Extended Abstracts on Human Factors in Computing Systems (CHI EA, CHI), pages 830-831, New York. ACM.
  17. Tintarev, N. and Masthoff, J. (2011). Designing and evaluating explanations for recommender systems. In Ricci, F., Rokach, L., Shapira, B., and Kantor, P. B., editors, Recommender Systems Handbook, pages 479- 510. Springer, US.
  18. Uehara, R. and Uno, Y. (2004). Efficient Algorithms for the Longest Path Problem. In Algorithms and Computation - 15th International Symposium (ISAAC 7804), pages 871-883, Hong Kong. Springer.
  19. Viswanathan, V. and Krishnamurthi, I. (2012). Finding relevant semantic association paths through user-specific intermediate entities. Human-centric Computing and Information Sciences, 2(9).
  20. Viswanathan, V. and Krishnamurthi, I. (2015). Finding relevant semantic association paths using semantic ant colony optimization algorithm. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 19(1):251-260.
  21. Wick, M. R. and Thompson, W. B. (1992). Reconstructive expert system explanation. Artificial Intelligence , 54(1-2):33-70.
Download


Paper Citation


in Harvard Style

N. Michalke V. and Hartig K. (2016). Explanation Retrieval in Semantic Networks - Understanding Spreading Activation based Recommendations . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 291-298. DOI: 10.5220/0006050502910298


in Bibtex Style

@conference{kdir16,
author={Vanessa N. Michalke and Kerstin Hartig},
title={Explanation Retrieval in Semantic Networks - Understanding Spreading Activation based Recommendations},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={291-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006050502910298},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Explanation Retrieval in Semantic Networks - Understanding Spreading Activation based Recommendations
SN - 978-989-758-203-5
AU - N. Michalke V.
AU - Hartig K.
PY - 2016
SP - 291
EP - 298
DO - 10.5220/0006050502910298