Dynamic Context-Aware Event Recognition Based on Markov Logic Networks
Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/3/491 |
| _version_ | 1811263022914600960 |
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| author | Fagui Liu Dacheng Deng Ping Li |
| author_facet | Fagui Liu Dacheng Deng Ping Li |
| author_sort | Fagui Liu |
| collection | DOAJ |
| description | Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data. |
| first_indexed | 2024-04-12T19:37:28Z |
| format | Article |
| id | doaj.art-3e3517d31035424e87bb8585858d082e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:37:28Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3e3517d31035424e87bb8585858d082e2022-12-22T03:19:10ZengMDPI AGSensors1424-82202017-03-0117349110.3390/s17030491s17030491Dynamic Context-Aware Event Recognition Based on Markov Logic NetworksFagui Liu0Dacheng Deng1Ping Li2School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaEvent recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data.http://www.mdpi.com/1424-8220/17/3/491event recognitionsensing datainformation fusionMarkov logic networksdynamic uncertainty |
| spellingShingle | Fagui Liu Dacheng Deng Ping Li Dynamic Context-Aware Event Recognition Based on Markov Logic Networks Sensors event recognition sensing data information fusion Markov logic networks dynamic uncertainty |
| title | Dynamic Context-Aware Event Recognition Based on Markov Logic Networks |
| title_full | Dynamic Context-Aware Event Recognition Based on Markov Logic Networks |
| title_fullStr | Dynamic Context-Aware Event Recognition Based on Markov Logic Networks |
| title_full_unstemmed | Dynamic Context-Aware Event Recognition Based on Markov Logic Networks |
| title_short | Dynamic Context-Aware Event Recognition Based on Markov Logic Networks |
| title_sort | dynamic context aware event recognition based on markov logic networks |
| topic | event recognition sensing data information fusion Markov logic networks dynamic uncertainty |
| url | http://www.mdpi.com/1424-8220/17/3/491 |
| work_keys_str_mv | AT faguiliu dynamiccontextawareeventrecognitionbasedonmarkovlogicnetworks AT dachengdeng dynamiccontextawareeventrecognitionbasedonmarkovlogicnetworks AT pingli dynamiccontextawareeventrecognitionbasedonmarkovlogicnetworks |