An ontology supported hybrid approach for recommendation in emergency situations
Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mehla, Sonia [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Institut Mines-Télécom and Springer Nature Switzerland AG 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of telecommunications - Springer International Publishing, 1946, 75(2020), 7-8 vom: 05. Juli, Seite 421-435 |
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| Übergeordnetes Werk: |
volume:75 ; year:2020 ; number:7-8 ; day:05 ; month:07 ; pages:421-435 |
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| DOI / URN: |
10.1007/s12243-020-00786-z |
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OLC2118785453 |
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| 520 | |a Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. | ||
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| 650 | 4 | |a Emergency situations | |
| 650 | 4 | |a Hybrid reasoning | |
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10.1007/s12243-020-00786-z doi (DE-627)OLC2118785453 (DE-He213)s12243-020-00786-z-p DE-627 ger DE-627 rakwb eng 620 VZ Mehla, Sonia verfasserin aut An ontology supported hybrid approach for recommendation in emergency situations 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Decision support system Ontology Rule based reasoning Case based reasoning Emergency situations Hybrid reasoning Jain, Sarika aut Enthalten in Annals of telecommunications Springer International Publishing, 1946 75(2020), 7-8 vom: 05. Juli, Seite 421-435 (DE-627)129514497 (DE-600)210938-4 (DE-576)014923726 0003-4347 nnns volume:75 year:2020 number:7-8 day:05 month:07 pages:421-435 https://doi.org/10.1007/s12243-020-00786-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW AR 75 2020 7-8 05 07 421-435 |
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10.1007/s12243-020-00786-z doi (DE-627)OLC2118785453 (DE-He213)s12243-020-00786-z-p DE-627 ger DE-627 rakwb eng 620 VZ Mehla, Sonia verfasserin aut An ontology supported hybrid approach for recommendation in emergency situations 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Decision support system Ontology Rule based reasoning Case based reasoning Emergency situations Hybrid reasoning Jain, Sarika aut Enthalten in Annals of telecommunications Springer International Publishing, 1946 75(2020), 7-8 vom: 05. Juli, Seite 421-435 (DE-627)129514497 (DE-600)210938-4 (DE-576)014923726 0003-4347 nnns volume:75 year:2020 number:7-8 day:05 month:07 pages:421-435 https://doi.org/10.1007/s12243-020-00786-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW AR 75 2020 7-8 05 07 421-435 |
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10.1007/s12243-020-00786-z doi (DE-627)OLC2118785453 (DE-He213)s12243-020-00786-z-p DE-627 ger DE-627 rakwb eng 620 VZ Mehla, Sonia verfasserin aut An ontology supported hybrid approach for recommendation in emergency situations 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Decision support system Ontology Rule based reasoning Case based reasoning Emergency situations Hybrid reasoning Jain, Sarika aut Enthalten in Annals of telecommunications Springer International Publishing, 1946 75(2020), 7-8 vom: 05. Juli, Seite 421-435 (DE-627)129514497 (DE-600)210938-4 (DE-576)014923726 0003-4347 nnns volume:75 year:2020 number:7-8 day:05 month:07 pages:421-435 https://doi.org/10.1007/s12243-020-00786-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW AR 75 2020 7-8 05 07 421-435 |
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10.1007/s12243-020-00786-z doi (DE-627)OLC2118785453 (DE-He213)s12243-020-00786-z-p DE-627 ger DE-627 rakwb eng 620 VZ Mehla, Sonia verfasserin aut An ontology supported hybrid approach for recommendation in emergency situations 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Decision support system Ontology Rule based reasoning Case based reasoning Emergency situations Hybrid reasoning Jain, Sarika aut Enthalten in Annals of telecommunications Springer International Publishing, 1946 75(2020), 7-8 vom: 05. Juli, Seite 421-435 (DE-627)129514497 (DE-600)210938-4 (DE-576)014923726 0003-4347 nnns volume:75 year:2020 number:7-8 day:05 month:07 pages:421-435 https://doi.org/10.1007/s12243-020-00786-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW AR 75 2020 7-8 05 07 421-435 |
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10.1007/s12243-020-00786-z doi (DE-627)OLC2118785453 (DE-He213)s12243-020-00786-z-p DE-627 ger DE-627 rakwb eng 620 VZ Mehla, Sonia verfasserin aut An ontology supported hybrid approach for recommendation in emergency situations 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Decision support system Ontology Rule based reasoning Case based reasoning Emergency situations Hybrid reasoning Jain, Sarika aut Enthalten in Annals of telecommunications Springer International Publishing, 1946 75(2020), 7-8 vom: 05. Juli, Seite 421-435 (DE-627)129514497 (DE-600)210938-4 (DE-576)014923726 0003-4347 nnns volume:75 year:2020 number:7-8 day:05 month:07 pages:421-435 https://doi.org/10.1007/s12243-020-00786-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW AR 75 2020 7-8 05 07 421-435 |
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Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 |
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Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 |
| abstract_unstemmed |
Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. © Institut Mines-Télécom and Springer Nature Switzerland AG 2020 |
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An ontology supported hybrid approach for recommendation in emergency situations |
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https://doi.org/10.1007/s12243-020-00786-z |
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Jain, Sarika |
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Jain, Sarika |
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| doi_str |
10.1007/s12243-020-00786-z |
| up_date |
2024-07-03T21:27:49.266Z |
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Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. 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