Some results of the functioning and construction of question-answering sensor systems

V. A. Mochalov, A. V. Mochalova

Abstract


The paper deals with the functioning and construction of question-answering sensor systems (QASS), which allow with the help of question-answer agents to answer to specified types of natural language questions based on environmental monitoring (EM) data from sensor networks and existing data analysis systems. Question-answer agents perform the following functions: collecting information from sensor network nodes; interaction with existing EM systems, with unstructured and textual data sources; adding data to ontologies and databases; form tasks and requests for data sources; form answers to requests and tasks. Question-answer agents carry out the conversion of a task / request into a request in the language of interaction with the EM system and after forming the answer send it to the task assignment coordinator. The use of agents is considered not only at the stage of answering a question, but also at the stage of constructing the structure of the QASS when searching for a sequence for removing unnecessary elements. The scheme of work of QASS is given and the application of the semantic analyzer in the architecture of QASS is considered. The current semantic analyzer is based on the Java programming language, the Drools expert system and the Apache Jena semantic platform.

Full Text:

PDF (Russian)

References


Stat'ja 77 Federal'nogo zakona ot 10 janvarja 2002 g. N 7-FZ "Ob ohrane okruzhajushhej sredy".

Faludi R. Building Wireless Sensor Networks. O'Reilly Media, 2010. 320 p.

Mochalov V.A.. Multi-agent Bio-inspired Algorithms for Wireless Sensor Network Design // The IEEE 17th International Conference on Advanced Communication Technology, 2015. Proceedings. ICACT. Phoenix Park, Korea, 2015. P.33-42.

Koucheryavy A., Vladyko A., Kirichek R. State of the Art and Research Challenges for Public Flying Ubiquitous Sensor Networks // Lecture Notes in Computer Science. 2015. Vol. 9247. P. 299-308.

Weng-Fong C., Tzu-Hsuan L., Yu-Cheng L. A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies // Sensors. 2018. Vol. 18(2).

Vermesan O., Friess O.. Internet of Things – From Research and Innovation to Market Deployment. River Publishers, 2014. 374 p.

Hanes D., Salgueiro D., Grossetete D., Barton D., Henry D. IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things. Cisco Press, 2017. 576 p.

Raj P., Raman A.C. The Internet of Things: Enabling Technologies, Platforms, and Use Cases. CRC Press, 2017. 364 p.

Semantic Sensor Network Ontology W3C Recommendation. 19 October 2017. URL: https://www.w3.org/TR/vocab-ssn/

Mochalov V.A., Mochalova A.V. Algorithms for changing the structure of geospace self-organizing question-answering sensor networks // Solar-Terrestrial Relations and Physics of Earthquake Precursors, E3S Web Conf. 2017. Vol. 20. P. 11.

Calbimonte J., Jeung H., Corcho O., Aberer K. Semantic Sensor Data Search in a Large-Scale Federated Sensor Network // Proceedings of the 4th International Workshop on Semantic Sensor Networks. 2011. Vol. 839. P. 23-38.

Wang X., Zhang X., Li M. A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query // International Journal of u- and e- Service, Science and Technology. 2015. Vol. 8, No. 10. P. 325-342,

Gladkov L.A., Kurejchik V.V., Kurejchik V.M., Sorokoletov P.V. Bioinspirirovannye metody v optimizacii. M.: Fizmatlit, 2009. 384 s.

Brabazon A., O'Neill M., McGarraghy S. Natural Computing Algorithms. Springer, 2015. 554 p.

Mandal J.K., Mukhopadhyay S., Pal T. Handbook of Research on Natural Computing for Optimization Problems (2 Volumes). Igi-Global, 2016. 1015 p.

Fister I., Xin-She Y., Fister I.., Brest J., Fister D. A Brief Review of Nature-Inspired Algorithms for Optimization // Elektrotehniski vestnik, 2013. Vol. 80(3). P. 1–7.

Binitha S., Sathya S. A Survey of Bio inspired Optimization Algorithms // International Journal of Soft Computing and Engineering (IJSCE). 2012. Vol. 2 (2). P. 137-151.

Gupta G. Monitoring Water Distribution Network using Machine Learning, EP242X. Degree Project in Communication Networks. 2017. 66 p. URL: http://kth.diva-portal.org/smash/get/diva2:1177842/FULLTEXT01.pdf

Gounaris C.E., Rajendran K., Kevrekidis I.G., Floudas C.A. Designing networks: A mixed integer linear optimization approach. 2015. 56 p. URL: https://arxiv.org/pdf/1502.00362.pdf

Taccari L. Mixed-integer programming models and methods for bilevel fair network optimization and energy cogeneration planning. PhD dissertation. 2015. 209 p.

Fraccaroli E., Quaglia D. Toolchain for Optimal Network Synthesis. URL: http://www.di.univr.it/documenti/OccorrenzaIns/matdid/

matdid014072.pdf

Mochalov V.A., Mochalova A.V. Application of «Sensor signal analysis network» complex for distributed, time synchronized analysis of electromagnetic radiation // Solar-Terrestrial Relations and Physics of Earthquake Precursors, E3S Web Conf. 2017. Vol. 20. P. 11.

SPARQL Query Language for RDF [Jelektronnyj resurs]. URL: https://www.w3.org/TR/rdf-sparql-query/

Boguslavskij I.M. Semanticheskij analiz i otvety na voprosy: sistema v stadii razrabotki / I.M. Boguslavskij, V.G. Dikonov, L.L. Iomdin, A.V. Lazurskij i dr. // Komp'juternaja lingvistika i intellektual'nye tehnologii: Po materialam ezhegodnoj Mezhdunarodnoj konferencii «Dialog» (Moskva, 27–30 maja 2015 g.). Vyp. 14 (21): V 2 t. – M.: Izd-vo RGGU, 2015. – T. 1. – C. 62 – 79.

Mochalova A.V. Semanticheskij analizator russkojazychnogo teksta dlja voprosno-otvetnoj sistemy: dis. kand. tehn. nauk: 05.13.18. Petrozavodsk. 2017. 128 c.

Mochalova A.V., Mochalov V.A. Mathematical model of an ontological-semantic analyzer using basic ontological-semantic patterns // Lecture Notes in Artificial Intelligence. Proceedings of 15th Mexican International Conference on Artificial Intelligence. – 2016. P. 53–66.

Jekspertnaja sistema Drools [Jelektronnyj resurs]. URL: https://www.drools.org (data obrashhenija 17.02.2018).

Lingvisticheskaja ontologija «Tezaurus RuTez» [Jelektronnyj resurs]. URL: http://www.labinform.ru/pub/ruthes/index.htm (data obrashhenija 17.02.2018)

Apache Jena [Jelektronnyj resurs]. https://jena.apache.org/ (data obrashhenija 17.02.2018).

Mochalova A.V., Mochalov V.A. Programmnaja realizacija na baze platformy Apache Jena voprosno-otvetnoj sistemy, ispol'zujushhej dannye ontologij // Komp'juternaja lingvistika i vychislitel'nye ontologii. Vypusk 2: Trudy XXI Mezhdunarodnoj ob"edinennoj konferencii «Internet i sovremennoe obshhestvo, IMS-2018, Sankt-Peterburg, 30 maja - 2 ijunja 2018 g. Sbornik nauchnyh statej. — SPb: Universitet ITMO, 2018.

Mochalov V. Seeding programming // Proceedings of the 2nd International Conference on BioGeoSciences. URL: https://www.researchgate.net/publication/325070229_Seeding_programming (preprint).


Refbacks

  • There are currently no refbacks.


Abava   FRUCT 2019

ISSN: 2307-8162