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Set Semantic Similarity for Image Prosthetic Knowledge Exchange

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Book cover Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11624))

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Abstract

Concept information can be expressed by text, images or general objects which semantic meaning is clear to a human in a specific cultural context. For a computer, when available, text with its semantics (e.g., metadata, comments, captions) can convey more precise meaning than images or general objects with low-level features (e.g., color distribution, shapes, sound peaks) to extract the concept underlying the object. Among semantic measures, web-based proximity measures e.g., confidence, PMING, NGD, Jaccard, Dice, are particularly useful for concept evaluation, exploiting statistical data provided by search engines on terms and expressions provided in texts associated with the object.

Where Artificial Intelligence can be a support for impaired individuals, e.g., having disabilities related to vision and hearing, understanding the concept underlying an object can be critical for an intelligent artificial assistant. In this work we propose to use the set semantic distance, already used in literature for semantic similarity measurement of web objects, as a tool for artificial assistants to support knowledge extraction; in other words, as prosthetic knowledge.

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References

  1. Franzoni, V., Milani, A., Nardi, D., Vallverdú, J.: Emotional machines: the next revolution. Web Intell. 17, 1–7 (2019)

    Article  Google Scholar 

  2. Franzoni, V., Milani, A., Vallverdú, J.: Emotional affordances in human-machine interactive planning and negotiation. In: Proceedings of 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (2017)

    Google Scholar 

  3. Milani, A., Rajdeep, N., Mangal, N., Mudgal, R.K., Franzoni, V.: Sentiment extraction and classification for the analysis of users’ interest in tweets. Int. J. Web Inf. Syst. 14, 29–40 (2018)

    Google Scholar 

  4. Mudgal, R.K., Niyogi, R., Milani, A., Franzoni, V.: Analysis of tweets to find the basis of popularity based on events semantic similarity. Int. J. Web Inf. Syst. 14, 438–452 (2018)

    Article  Google Scholar 

  5. Franzoni, V., Mengoni, P., Milani, A.: Dimensional morphing interface for dynamic learning evaluation. In: Information Visualisation - Biomedical Visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2018 (2018)

    Google Scholar 

  6. Gervasi, O., Franzoni, V., Riganelli, M., Tasso, S.: Automating facial emotion recognition. Web Intell. 17, 17–27 (2019)

    Article  Google Scholar 

  7. Zhang, L., Ma, W.-Y., Li, X., Lin, F., Chen, L.: Image annotation by large-scale content-based image retrieval (2007)

    Google Scholar 

  8. Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32, 13–47 (2006)

    Article  Google Scholar 

  9. Milani, A., Franzoni, V., Biondi, G., Li, Y.: Integrating binary similarity measures in the link prediction task (2019)

    Google Scholar 

  10. Franzoni, V., Chiancone, A., Milani, A.: A multistrain bacterial diffusion model for link prediction. Int. J. Pattern Recognit Artif Intell. 31, 1759024 (2017)

    Article  Google Scholar 

  11. Budanitsky, A., Hirst, G.: Semantic distance in WordNet : an experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and Other Lexical Resources, vol. 2 (2001)

    Google Scholar 

  12. Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:: similarity: measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004 (2004)

    Google Scholar 

  13. Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inf. Theory 37, 145–151 (1991)

    Article  MathSciNet  Google Scholar 

  14. Strube, M., Ponzetto, S.P.: WikiRelate! Computing semantic relatedness using Wikipedia. Am. Assoc. Artif. Intell. 6, 1419–1424 (2006)

    Google Scholar 

  15. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI International Joint Conference on Artificial Intelligence (2007)

    Google Scholar 

  16. Milne, D.: Computing semantic relatedness using Wikipedia link structure. In: Work (2007)

    Google Scholar 

  17. Franzoni, V., Milani, A.: Heuristic semantic walk: browsing a collaborative network with a search engine-based heuristic (2013)

    Google Scholar 

  18. Franzoni, V., Milani, A.: A pheromone-like model for semantic context extraction from collaborative networks. In: Proceedings of 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015 (2016)

    Google Scholar 

  19. Franzoni, V., Mencacci, M., Mengoni, P., Milani, A.: Semantic heuristic search in collaborative networks: measures and contexts. In: Proceedings of 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2014 (2014)

    Google Scholar 

  20. Franzoni, V., Milani, A.: Heuristic semantic walk for concept chaining in collaborative networks. Int. J. Web Inf. Syst. 10, 85–103 (2014)

    Article  Google Scholar 

  21. Franzoni, V., Mencacci, M., Mengoni, P., Milani, A.: Heuristics for semantic path search in Wikipedia (2014)

    Google Scholar 

  22. Franzoni, V., Milani, A.: Semantic context extraction from collaborative networks. In: Proceedings of the 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2015 (2015)

    Google Scholar 

  23. Pallottelli, S., Franzoni, V., Milani, A.: Multi-path traces in semantic graphs for latent knowledge elicitation. In: Proceedings of the International Conference on Natural Computation (2016)

    Google Scholar 

  24. Wu, L., Hua, X.-S., Yu, N., Ma, W.-Y., Li, S.: Flickr distance: a relationship measure for visual concepts. IEEE Trans. Pattern Anal. Mach. Intell. 34, 863–875 (2012)

    Article  Google Scholar 

  25. Liben-Nowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. JASIST 58, 1019–1031 (2007)

    Article  Google Scholar 

  26. Franzoni, V., Lepri, M., Li, Y., Milani, A.: Efficient graph-based author disambiguation by topological similarity in DBLP. In: Proceedings of 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018 (2018)

    Google Scholar 

  27. Adamic, L.A., Lento, T.M., Adar, E., Ng, P.C.: Information evolution in social networks. In: WSDM, pp. 473–482. ACM (2016)

    Google Scholar 

  28. Hoffman, M., Steinley, D., Brusco, M.J.: A note on using the adjusted Rand index for link prediction in networks. Soc. Netw. 42, 72–79 (2015)

    Article  Google Scholar 

  29. Han, S., Xu, Y.: Link prediction in microblog network using supervised learning with multiple features. JCP 11, 72–82 (2016)

    Article  Google Scholar 

  30. Biondi, G., Franzoni, V., Li, Y., Milani, A.: SEMO: a semantic model for emotion recognition in web objects. In: ICCSA. Springer, Heidelberg (2017)

    Google Scholar 

  31. Franzoni, V., Biondi, G., Milani, A.: A web-based system for emotion vector extraction. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10406, pp. 653–668. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62398-6_46

    Chapter  Google Scholar 

  32. Huang, A.H., Yen, D.C., Zhang, X.: Exploring the potential effects of emoticons. Inf. Manag. 45, 466–473 (2008)

    Article  Google Scholar 

  33. Turney, P.D.: Mining the web for synonyms: {PMI-IR} versus {LSA} on {TOEFL}. CoRR. cs.LG/0212 (2002)

    Google Scholar 

  34. Franzoni, V., Milani, A.: PMING distance: a collaborative semantic proximity measure. In: Proceedings of 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012 (2012)

    Google Scholar 

  35. Biondi, G., Franzoni, V., Li, Y., Milani, A.: Web-based similarity for emotion recognition in web objects. In: Proceedings of 9th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2016 (2016)

    Google Scholar 

  36. Franzoni, V., Milani, A., Biondi, G.: SEMO: a semantic model for emotion recognition in web objects. In: Proceedings of 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (2017)

    Google Scholar 

  37. Franzoni, V., Milani, A.: Structural and semantic proximity in information networks. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10404, pp. 651–666. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62392-4_47

    Chapter  Google Scholar 

  38. Chiancone, A., Franzoni, V., Li, Y., Markov, K., Milani, A.: Leveraging zero tail in neighbourhood for link prediction. In: Proceedings of 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015 (2016)

    Google Scholar 

  39. Chiancone, A., Milani, A., Poggioni, V., Pallottelli, S., Madotto, A., Franzoni, V.: A multistrain bacterial model for link prediction. In: Proceedings of International Conference on Natural Computation (2016)

    Google Scholar 

  40. Chiancone, A., Franzoni, V., Niyogi, R., Milani, A.: Improving link ranking quality by quasi-common neighbourhood. In: Proceedings of 15th International Conference on Computational Science and Its Applications, ICCSA 2015 (2015)

    Google Scholar 

  41. Franzoni, V., Milani, A.: A semantic comparison of clustering algorithms for the evaluation of web-based similarity measures. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9790, pp. 438–452. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42092-9_34

    Chapter  Google Scholar 

  42. Franzoni, V., Li, Y., Mengoni, P.: A path-based model for emotion abstraction on facebook using sentiment analysis and taxonomy knowledge. In: Proceedings of 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (2017)

    Google Scholar 

  43. Leung, C.H.C., Li, Y., Milani, A., Franzoni, V.: Collective evolutionary concept distance based query expansion for effective web document retrieval. In: Murgante, B., et al. (eds.) ICCSA 2013. LNCS, vol. 7974, pp. 657–672. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39649-6_47

    Chapter  Google Scholar 

  44. Brown, A., Randall, S., Ferrante, A., Boyd, J.: Partial Agreements in Probabilistic Linkages. Int. J. Popul. Data Sci. 3, 293 (2018)

    Google Scholar 

  45. Franzoni, V., Leung, C.H.C., Li, Y., Mengoni, P., Milani, A.: Set similarity measures for images based on collective knowledge. In: Gervasi, O., et al. (eds.) ICCSA 2015. LNCS, vol. 9155, pp. 408–417. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21404-7_30

    Chapter  Google Scholar 

  46. Zhang, J., Zhou, Q., Zhuo, L., Geng, W., Wang, S.: A CBIR system for hyperspectral remote sensing images using endmember extraction. IJWPRAI 31(4) (2016)

    Google Scholar 

  47. Franzoni, V., Milani, A., Pallottelli, S., Leung, C.H.C., Li, Y.: Context-based image semantic similarity. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 (2016)

    Google Scholar 

  48. Chan, S.W., Franzoni, V., Mengoni, P., Milani, A.: Context-based image semantic similarity for prosthetic knowledge. In: Proceedings of 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018 (2018)

    Google Scholar 

  49. Franzoni, V., Tasso, S., Pallottelli, S.: Sharing linkable learning objects between a content management system and a learning management system with the use of metadata and a taxonomy assistant for categorization. In: LNCS, ICCSA 2019 (2019)

    Google Scholar 

  50. Tasso, S., Pallottelli, S., Gervasi, O., Sabbatini, F., Franzoni, V.: Cloud and local servers into a federation of learning object repositories. In: ICCSA 2019, LNCS. Springer, Heidelberg (2019)

    Google Scholar 

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Acknowledgments

The authors thank the students involved in the experiments, and the authors of previous works of the image set similarity project, cited in this paper, in particular Alfredo Milani, Clement H.C. Leung, Sheung Wai Chan, Marco Mencacci, Paolo Mengoni, and Simonetta Pallottelli.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, 06123, Perugia, Italy

    Valentina Franzoni & Alfredo Milani

  2. Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, via Ariosto, 25, 00185, Rome, Italy

    Valentina Franzoni

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong, China

    Yuanxi Li

Authors
  1. Valentina Franzoni
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  2. Yuanxi Li
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  3. Alfredo Milani
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Corresponding author

Correspondence to Valentina Franzoni .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Franzoni, V., Li, Y., Milani, A. (2019). Set Semantic Similarity for Image Prosthetic Knowledge Exchange. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11624. Springer, Cham. https://doi.org/10.1007/978-3-030-24311-1_37

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  • DOI: https://doi.org/10.1007/978-3-030-24311-1_37

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