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RETRACTED CHAPTER: A Situation-Aware Method Based on Ontology Analysis of the Semantic Social Network

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Knowledge Science, Engineering and Management (KSEM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

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Abstract

Situation awareness relies on the situation context, classifying context and inferring further situation about context. However, the relevant applications have to deal with the inherent imperfection of situation recognition for decision making. The current researches of situation-aware focused on situation recognition based on modeling, classifying and apperceiving of context information, which were insufficiency in the researches and utilization in situation relevance. To overcome this deficiency, this paper fully analyzes the situation relevance (including the trigger and dependency among situations) based on situation ontology, and puts forward a situation-aware method based on ontology analysis of the semantic social network (SR-SSNOA). The main research includes that: (1) SR-SSNOA converts the situation ontology into different figures, and introduces semantic social network to analyze situation ontology. (2) SR-SSNOA realizes the recognition and recommendation of situation by synthetically considering the situation quality, the situation relevance and the community impact. Extensive experiments are carried out, which reveals the performance of SR-SSNOA at different parameter values. A questionnaire is conducted to evaluate the results, which further proves our method’s accuracy and correctness.

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Change history

  • 29 March 2019

    The authors have retracted this chapter [1] because the method described in the chapter is based on the wrong data set, which means that the experimental results cannot be reproduced. All authors agree with this retraction.

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Correspondence to Wenbin Hu .

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Hu, W., Wang, H. (2015). RETRACTED CHAPTER: A Situation-Aware Method Based on Ontology Analysis of the Semantic Social Network. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-25159-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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