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European Semantic Web Conference

ESWC 2022: The Semantic Web pp 289–304Cite as

Do Arduinos Dream of Efficient Reasoners?

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

The Semantic Web of Things enhances the Internet of Things with Web technologies as well as Knowledge Graphs and reasoning. Traditional reasoners are too heavy in terms of memory footprint and/or processing time to be implementable on things. In this work, we present LiRoT, a lightweight incremental reasoner that can be embedded in constrained objects, so that reasoning on them in a fog architecture becomes possible. The focus of this work is to reduce drastically memory footprint while paying attention to processing time, hence usual optimization techniques are not fully adequate. We provide evaluations that (i) compare our system to the state of the art and (ii) show the effective benefits of the different optimizations we have implemented.

Keywords

  • Semantic web
  • Reasoning
  • Web of things
  • Embedded systems
  • Optimization

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  • DOI: 10.1007/978-3-031-06981-9_17
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Notes

  1. 1.

    Microcontrollers are small processing units designed to run embedded applications, in contrast to more powerful microprocessors that can execute general purpose applications.

  2. 2.

    https://www.w3.org/TR/rdf-mt/#rdfs_entailment.

  3. 3.

    https://coswot.gitlab.io/.

  4. 4.

    https://www.w3.org/TR/wot-usecases/#Agricultural-irrigation.

  5. 5.

    https://www.w3.org/TR/wot-usecases/#edge-computing.

  6. 6.

    https://www.w3.org/TR/owl2-profiles/#OWL_2_RL.

  7. 7.

    https://en.wikipedia.org/wiki/Logic_programming.

  8. 8.

    https://www.w3.org/TR/rdf11-mt/#entailment-rules-informative.

  9. 9.

    As we herein consider the KB as being an ontology expressed in OWL 2 RL under RDF-based semantics, facts are RDF triples.

  10. 10.

    http://www.clipsrules.net/.

  11. 11.

    https://graphdb.ontotext.com/documentation/free/reasoning.html#retraction-of-assertions.

  12. 12.

    https://github.com/drobilla/sord.

  13. 13.

    https://rdf.js.org/.

  14. 14.

    https://github.com/drobilla/serd.

  15. 15.

    https://github.com/troydhanson/uthash.

  16. 16.

    Rulesets are described at https://jena.apache.org/documentation/inference/#RDFSconfiguration.

  17. 17.

    The maximum amount of RAM used by a program throughout its execution.

  18. 18.

    Indeed, these devices have, in addition to a limited memory size for handling the application data (DRAM), the same kind of limitations for storing the program itself (IRAM). The reasoner should also be compiled specifically for the targeted platform, and use platform-specific available libraries.

References

  1. Bassiliades, N., Vlahavas, I.: R-device: a deductive RDF rule language. In: Antoniou, G., Boley, H. (eds.) RuleML 2004. LNCS, vol. 3323, pp. 65–80. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30504-0_6

    CrossRef  Google Scholar 

  2. Bobed, C., Yus, R., Bobillo, F., Mena, E.: Semantic reasoning on mobile devices: do androids dream of efficient reasoners? J. Web Seman. 35, 167–183 (2015)

    CrossRef  Google Scholar 

  3. Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 74–83 (2004)

    Google Scholar 

  4. Dell’Aglio, D., Della Valle, E., van Harmelen, F., Bernstein, A.: Stream reasoning: a survey and outlook. Data Sci. 1(1–2), 59–83 (2017)

    CrossRef  Google Scholar 

  5. Doorenbos, R.B.: Production Matching for Large Learning Systems. Carnegie-Mellon Univ Pittsburgh PA Dept of Computer Science, Technical report (1995)

    Google Scholar 

  6. Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. In: Readings in Artificial Intelligence and Databases, pp. 547–559. Elsevier (1989)

    Google Scholar 

  7. Cuenca Grau, B., Halaschek-Wiener, C., Kazakov, Y.: History matters: incremental ontology reasoning using modules. In: Aberer, K. (ed.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 183–196. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_14

    CrossRef  Google Scholar 

  8. Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logic. In: Proceedings of the 12th International Conference on World Wide Web, pp. 48–57 (2003)

    Google Scholar 

  9. Guo, Y., Pan, Z., Heflin, J.: Lubm: a benchmark for owl knowledge base systems. J. Web Seman. 3(2–3), 158–182 (2005)

    CrossRef  Google Scholar 

  10. Gupta, A., Mumick, I.S., Subrahmanian, V.S.: Maintaining views incrementally. ACM SIGMOD Rec. 22(2), 157–166 (1993)

    CrossRef  Google Scholar 

  11. Matsakis, N.D., Klock, F.S.: The rust language. ACM SIGAda Ada Lett. 34(3), 103–104 (2014)

    CrossRef  Google Scholar 

  12. Meditskos, G., Bassiliades, N.: Clips-owl: a framework for providing object-oriented extensional ontology queries in a production rule engine. Data Knowl. Eng. 70(7), 661–681 (2011)

    CrossRef  Google Scholar 

  13. Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: a highly-scalable RDF store. In: Arenas, M. (ed.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25010-6_1

    CrossRef  Google Scholar 

  14. Oliya, M., Pung, H.K.: Towards incremental reasoning for context aware systems. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) ACC 2011. CCIS, vol. 190, pp. 232–241. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22709-7_24

    CrossRef  Google Scholar 

  15. Seitz, C., Schönfelder, R.: Rule-based OWL reasoning for specific embedded devices. In: Aroyo, L. (ed.) ISWC 2011. LNCS, vol. 7032, pp. 237–252. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25093-4_16

    CrossRef  Google Scholar 

  16. Tai, W., Keeney, J., O’Sullivan, D.: Resource-constrained reasoning using a reasoner composition approach. Seman. Web 6(1), 35–59 (2015)

    CrossRef  Google Scholar 

  17. Terdjimi, M., Médini, L., Mrissa, M.: Hylar+ improving hybrid location-agnostic reasoning with incremental rule-based update. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 259–262 (2016)

    Google Scholar 

  18. Terdjimi, M., Médini, L., Mrissa, M.: Web reasoning using fact tagging. In: Companion Proceedings of the The Web Conference 2018, pp. 1587–1594 (2018)

    Google Scholar 

  19. Urbani, J., Margara, A., Jacobs, C., van Harmelen, F., Bal, H.: DynamiTE: parallel materialization of dynamic RDF data. In: Alani, H. (ed.) ISWC 2013. LNCS, vol. 8218, pp. 657–672. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_41

    CrossRef  Google Scholar 

  20. Van Woensel, W., Abidi, S.S.R.: Optimizing semantic reasoning on memory-constrained platforms using the RETE algorithm. In: Gangemi, A. (ed.) ESWC 2018. LNCS, vol. 10843, pp. 682–696. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_44

    CrossRef  Google Scholar 

  21. Wright, I., Marshall, J.A.: The execution kernel of rc++: Rete*, a faster rete with treat as a special case. Int. J. Intell. Game. Simul. 2(1), 36–48 (2003)

    Google Scholar 

  22. Yousefpour, A., et al.: All one needs to know about fog computing and related edge computing paradigms: a complete survey. J. Syst. Architect. 98, 289–330 (2019)

    CrossRef  Google Scholar 

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Acknowledgment

This work is supported by grant ANR-19-CE23-0012 from the Agence Nationale de la Recherche, France, for the CoSWoT project.

Author information

Authors and Affiliations

  1. Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, 69621, Villeurbanne, France

    Alexandre Bento, Lionel Médini & Frédérique Laforest

  2. Univ Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Étienne, France

    Kamal Singh

Authors
  1. Alexandre Bento
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  2. Lionel Médini
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  3. Kamal Singh
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  4. Frédérique Laforest
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Corresponding author

Correspondence to Alexandre Bento .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Dr. Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Bento, A., Médini, L., Singh, K., Laforest, F. (2022). Do Arduinos Dream of Efficient Reasoners?. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_17

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