Preference Dissemination by Sharing Viewpoints - Simulating Serendipity

Guillaume Surroca, Philippe Lemoisson, Clément Jonquet, Stefano Cerri

2015

Abstract

The Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several “viewpoints” are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set.

References

  1. Aberer, K., Cudr, P., Catarci, T., Hacid, M., Illarramendi, A., Mecella, M., … Scannapieco, M. (2004). Emergent Semantics Principles and Issues. In D. Lee, YoonJoon and Li, Jianzhong and Whang, Kyu-Young and Lee (Ed.), Database Systems for Advanced Applications (Vol. 2, pp. 25-38). Springer Berlin Heidelber.
  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
  3. Bizer, C., Health, T., & Berners-Lee, T. (2009). Linked Data - The Story So Far. In Semantic Services, Interoperability and Web Applications: Emerging Concepts (Vol. 5, pp. 1-22).
  4. Bowles, M. (2004). Relearning to E-learn: Strategies for Electronic Learning and Knowledge. Educational Technology & Society, 7(4), 212-220.
  5. Corneli, J., Pease, A., & Colton, S. (2014). Modelling serendipity in a computational context. arXiv Preprint arXiv:1411.0440.
  6. Edelman, G. (1987). Neural Darwinism: The theory of neuronal group selection.
  7. Fine, G. A., & Deegan, J. G. (1996). Three principles of Serendip: insight, chance, and discovery in qualitative research. International Journal of Qualitative Studies in Education, 9(4), 434-447.
  8. Freddo, A. R., & Tacla, C. A. (2009). Integrating social web with semantic web?: ontology learning and ontology evolution from folksonomies. KEOD 2009 Proceedings, 247-253.
  9. Gruber, T. (2008). Collective knowledge systems: Where the Social Web meets the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(1), 4-13.
  10. Karapiperis, S., & Apostolou, D. (2006). Consensus building in collaborative ontology engineering processes. Journal of Universal Knowledge Management, 199-216.
  11. Lee, W.-N., Shah, N., Sundlass, K., & Musen, M. (2008). Comparison of ontology-based semantic-similarity measures. AMIA, Annual Symposium 2008, 384-8.
  12. Lemoisson, P., Surroca, G., & Cerri, S. (2013). Viewpoints?: an alternative approach toward Business Intelligence. In eChallenges e-2013 (p. 8).
  13. Limpens, F., & Gandon, F. (2011). Un cycle de vie complet pour l 78 enrichissement sémantique des folksonomies. In Extraction Gestion de Connaissance EGC 2011 (pp. 389-400).
  14. Marchionini, G. (1997). Information Seeking in Electronic Environments (Cambridge., p. 224). Cambridge university press.
  15. Merton, R. K., & Barber, E. (2006). The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science (Princeton., Vol. 2006, p. 313).
  16. Lee, S. (2010). Learning the emergent knowledge from annotated blog postings. Web Semantics: Science, Services and Agents on the World Wide Web, 8(4), 329-339.
  17. Pedersen, T., Pakhomov, S. V. S., Patwardhan, S., & Chute, C. G. (2007). Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics, 40(3), 288-99.
  18. Perriault, J. (2000). Effet diligence, effet serendip et autres défis pour les sciences de l'information. In Pratiques collectives distribuées sur Internet.
  19. Surroca, G., Lemoisson, P., Jonquet, C., & Cerri, S. A. (2014, May 13). Construction et évolution de connaissances par confrontation de points de vue : prototype pour la recherche d'information scientifique. IC - 25èmes Journées Francophones d'Ingénierie Des Connaissances.
  20. Tough, A. (1999). Reflections on the Study of Adult Learning. WALL Working Paper.
  21. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes (Vol. 1978, p. 159).
  22. Yamaba, H., Tanoue, M., & Takatsuka, K. (2013). On a serendipity-oriented recommender system based on folksonomy. Procedia Computer Science, 22, 276- 284.
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Paper Citation


in Harvard Style

Surroca G., Lemoisson P., Jonquet C. and Cerri S. (2015). Preference Dissemination by Sharing Viewpoints - Simulating Serendipity . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 402-409. DOI: 10.5220/0005636204020409


in Bibtex Style

@conference{keod15,
author={Guillaume Surroca and Philippe Lemoisson and Clément Jonquet and Stefano Cerri},
title={Preference Dissemination by Sharing Viewpoints - Simulating Serendipity},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={402-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005636204020409},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Preference Dissemination by Sharing Viewpoints - Simulating Serendipity
SN - 978-989-758-158-8
AU - Surroca G.
AU - Lemoisson P.
AU - Jonquet C.
AU - Cerri S.
PY - 2015
SP - 402
EP - 409
DO - 10.5220/0005636204020409