A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure
Abstract
This paper describes the creation of a domain ontology to represent knowledge to populate a knowledge base to be used by agents, in the environment of Internet Infrastructure routing domains. Protégé 5 was used, which produces results suitable for both software-developed agents and humans. The knowledge created with Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are presented and the ontology is made available for public use.The content produced with Protégé 5 will be used to populate the knowledge base of the Structure for Knowledge Acquisition, Use, Learning and Collaboration (SKAU), an environment to support intelligent agents over Internet Autonomous Systems domains.
Keywords:
internet infrastructure, autonomous systems, ontology, knowledge base, intelligent agents
References
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ARDJANI, F.; BOUCHIHA, D.; MALKI, M. Ontology-alignment techniques: Survey and analysis. International Journal of Modern Education & Computer Science, v. 7, n. 11, 2015.
ASIM, M. N.; WASIM, M.; KHAN, M. U. G.; MAHMOOD, W.; ABBASI, H. M. A survey of ontology learning techniques and applications. Database, Narnia, v. 2018, 2018.
BELLE, V.; PAPANTONIS, I. Principles and practice of explainable machine learning. arXiv preprint arXiv:2009.11698, 2020.
BHAVSAR, K.; KUMAR, N.; DANGETI, P. Natural language processing with python cookbook: Over 60 recipes to implement text analytics solutions using deep learning principles. Packt Publishing, 2017.
BIRD, S.; KLEIN, E.; LOPER, E. Natural language processing with Python. [S.l.]: " O’Reilly Media, Inc.", 2009.
BORST, W. N. Construction of engineering ontologies for knowledge sharing and reuse. Tese (Doutorado) — University of Twente, 1997.
BRAGA, J. Ambiente para Aquisição de Conhecimento por Agentes em Domínios Restritos na Infraestrutura da Internet. Tese (Doutorado)—Instituto Superior Técnico & Universidade Presbiteriana Mackenzie, 2019. DOI: 10.31237/osf.io/nzmtf, Availaible in https://thesiscommons.org/nzmtf/.
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BRAGA, J.; REGATEIRO, F.; DIAS, J. Machine Learning Ontology (MLOnto) Repository. 2020. Project Repository: OSF. DOI: 10.17605/OSF.IO/CHU5Q. Available in:.
BRAGA, J.; SILVA, J. N.; ENDO, P.; OMAR, N. Structure for knowledge acquisition, use, learning and collaboration inter agents over internet infrastructure domains: Proceedings of the 2019 computing conference. In: ARAI, K.; BHATIA, R.; KAPOOR, S. (Ed.). Intelligent Computing. [S.l.]: Springer International Publishing, 2019. v. 1, p. 527–547. Doi: 10.1007/978-3-030-22871-2, ISBN: 978-3-030-22871-2.
BRANK, J.; GROBELNIK, M.; MLADENIC, D. A survey of ontology evaluation techniques. In: Proceedings of the conference on data mining and data warehouses (SiKDD 2005). Ljubljana, Slovenia: SiKDD, 2005. p. 166–170.
BUITELAAR, P.; CIMIANO, P.; MAGNINI, B. Ontology learning from text: An overview. Ontology learning from text: Methods, evaluation and applications, IOS Press, Amsterdam, v. 123, p. 3–12, 2005.
BUITELAAR, P.; OLEJNIK, D.; SINTEK, M. A protégé plug-in for ontology extraction from text based on linguistic analysis. In: SPRINGER. European Semantic Web Symposium. [S.l.], 2004. p. 31–44.
CARLSON, A.; BETTERIDGE, J.; KISIEL, B.; SETTLES, B.; HRUSCHKA, E. R.; MITCHELL, T. M. Toward an architecture for never-ending language learning. In: Twenty-Fourth AAAI Conference on Artificial Intelligence. Atlanta, US: AAAI Publications, 2010. p. 8.
CIMIANO, P.; VÖLKER, J. A framework for ontology learning and data-driven change discovery. In: SPRINGER. Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB). [S.l.], 2005. p. 227–238.
DOAN, A.; MADHAVAN, J.; DOMINGOS, P.; HALEVY, A. Ontology matching: A machine learning approach. In: Handbook on ontologies. [S.l.]: Springer, 2004. p. 385–403.
EHRIG, M. Ontology Alignment: Bridging the Semantic Gap. 1. ed. Germany: Springer, 2007.
FU, H.; CHI, Z.; FENG, D.; SONG, J. Machine learning techniques for ontology-based leaf classification. In: IEEE. ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004. [S.l.], 2004. v. 1, p. 681–686.
GOLBREICH, C.; DAMERON, O.; GIBAUD, B.; BURGUN, A. Web ontology language requirements wrt expressiveness of taxonomy and axioms in medicine. In: SPRINGER. International Semantic Web Conference. [S.l.], 2003. p. 180–194.
GRUBER, T. R. A translation approach to portable ontology specifications. Knowledge Acquisition, v. 5, p. 199–220, 1993.
GUARINO, N.; OBERLE, D.; STAAB, S. What is an ontology? In: Handbook on ontologies. [S.l.]: Springer, 2009. p. 1–17.
GUIZZARDI, G.; WAGNER, G.; ALMEIDA, J. P. A.; GUIZZARDI, R. S. Towards ontological foundations for conceptual modeling: The unified foundational ontology (ufo) story. Applied ontology, IOS Press, v. 10, n. 3-4, p. 259–271, 2015.
HITZLER, P.; KRöTZSCH, M.; PARSIA, B.; PATEL-SCHNEIDER, P. F.; RUDOLPH, S. OWL 2 Web Ontology Language Primer. W3C Recommendation, v. 27, n. 1, p. 1–123, 2009. Available in: https://www.w3.org/TR/owl2-primer/
HORRIDGE, M. A Practical Guide To Building OWL Ontologies Using Protégé 4 and CO-ODE Tools Edition 1.3. [S.l.], 2011. 108 p. Disponível em: https://www.researchgate.net/publication/230585369_A_Practical_Guide_To_Building_OWL_Ontologies_Using_The_Prot%27eg%27e-OWL_Plugin_and_CO-ODE_Tools.
ICHISE, R. Machine learning approach for ontology mapping using multiple concept similarity measures. In: IEEE. Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). [S.l.], 2008. p. 340–346.
KALIBATIENE, D.; VASILECAS, O. Survey on ontology languages. In: SPRINGER. International Conference on Business Informatics Research. [S.l.], 2011. p. 124–141.
KONYS, A. Knowledge systematization for ontology learning methods. Procedia computer science, Elsevier, v. 126, p. 2194–2207, 2018.
MITCHELL, T.; COHEN, W.; HRUSCHKA, E.; TALUKDAR, P.; YANG, B.; BETTERIDGE, J.; CARLSON, A.; DALVI, B.; GARDNER, M.; KISIEL, B. et al. Never-ending learning. Communications of the ACM, v. 61, n. 5, p. 103–115, 2018.
MUSEN, M. A. The protégé project: a look back and a look forward. AI Matters, v. 1, n. 4, p. 4–12, 2015. Disponível em: https://doi.org/10.1145/2757001.2757003.
PEASE, A. Ontology. [S.l.]: Articulate Software Press, 2011. ISBN: 1889455105. POON, H.; DOMINGOS, P. Unsupervised ontology induction from text. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics. [S.l.: s.n.], 2010. p. 296–305.
REN, F. A demo for constructing domain ontology from academic papers. In: Proceedings of COLING 2012: Demonstration Papers. [S.l.: s.n.], 2012. p. 369–376.
SACHA, D.; KRAUS, M.; KEIM, D. A.; CHEN, M. Vis4ml: An ontology for visual analytics assisted machine learning. IEEE transactions on visualization and computer graphics, IEEE, v. 25, n. 1, p. 385–395, 2018.
SHAMSFARD, M.; BARFOROUSH, A. A. Learning ontologies from natural language texts. International journal of human-computer studies, Elsevier, v. 60, n. 1, p. 17–63, 2004.
SHEKHAR, M.; K, S. R. Semantic Web Search based on Ontology Modeling using Protege Reasoner. 2013.
SILVER, D. L.; YANG, Q.; LI, L. Lifelong machine learning systems: Beyond learning algorithms. In: 2013 AAAI spring symposium series. [S.l.: s.n.], 2013.
SLIMANI, T. Ontology development: A comparing study on tools, languages and formalisms. Indian Journal of Science and Technology, v. 8, n. 24, p. 1–12, 2015.
STUDER, R.; BENJAMINS, V. R.; FENSEL, D. Knowledge engineering: principles and methods. Data & knowledge engineering, Elsevier, v. 25, n. 1-2, p. 161–197, 1998.
TAN, H.; LAMBRIX, P. Selecting an ontology for biomedical text mining. In: Proceedings of the BioNLP 2009 Workshop. [S.l.: s.n.], 2009. p. 55–62.
TUDORACHE, T.; NYULAS, C.; NOY, N. F.; MUSEN, M. A. Webprotégé: A collaborative ontology editor and knowledge acquisition tool for the web. Semantic web, IOS Press, v. 4, n. 1, p. 89–99, 2013.
XIAO, G.; DING, L.; COGREL, B.; CALVANESE, D. Virtual knowledge graphs: An overview of systems and use cases. Data Intelligence, MIT Press, v. 1, n. 3, p. 201–223, 2019.
XIAO, G.; LANTI, D.; KONTCHAKOV, R.; KOMLA-EBRI, S.; GÜZEL-KALAYCI, E.; DING, L.; CORMAN, J.; COGREL, B.; CALVANESE, D.; BOTOEVA, E. The virtual knowledge graph system ontop. In: SPRINGER. International Semantic Web Conference. [S.l.], 2020. p. 259–277.
ARBIX, G. A trAnspArênciA no centro dA construção de umA iA éticA. Novos estudos CEBRAP, SciELO Brasil, v. 39, n. 2, p. 395–413, 2020.
ARDJANI, F.; BOUCHIHA, D.; MALKI, M. Ontology-alignment techniques: Survey and analysis. International Journal of Modern Education & Computer Science, v. 7, n. 11, 2015.
ASIM, M. N.; WASIM, M.; KHAN, M. U. G.; MAHMOOD, W.; ABBASI, H. M. A survey of ontology learning techniques and applications. Database, Narnia, v. 2018, 2018.
BELLE, V.; PAPANTONIS, I. Principles and practice of explainable machine learning. arXiv preprint arXiv:2009.11698, 2020.
BHAVSAR, K.; KUMAR, N.; DANGETI, P. Natural language processing with python cookbook: Over 60 recipes to implement text analytics solutions using deep learning principles. Packt Publishing, 2017.
BIRD, S.; KLEIN, E.; LOPER, E. Natural language processing with Python. [S.l.]: " O’Reilly Media, Inc.", 2009.
BORST, W. N. Construction of engineering ontologies for knowledge sharing and reuse. Tese (Doutorado) — University of Twente, 1997.
BRAGA, J. Ambiente para Aquisição de Conhecimento por Agentes em Domínios Restritos na Infraestrutura da Internet. Tese (Doutorado)—Instituto Superior Técnico & Universidade Presbiteriana Mackenzie, 2019. DOI: 10.31237/osf.io/nzmtf, Availaible in https://thesiscommons.org/nzmtf/.
BRAGA, J. Environment for Knowledge Acquisition by Agents in Internet InfrastructureRestricted Domains. Tese (Doutorado)—Instituto Superior Tecnico & Universidade Presbiteriana Mackenzie, 2019. DOI: 10.31237/osf.io/83ztf. English version translated by author. Available in https://thesiscommons.org/83ztf/.
BRAGA, J.; DIAS, J. L. R.; REGATEIRO, F. A machine learing ontology. Frenxiv, Oct 2020. Preprint. DOI: 10.31226/osf.io/rc954. Available at https://frenxiv.org/rc954/.
BRAGA, J.; NOBRE, J. C.; GRANVILLE, L. Z.; SANTOS, M. Como Protocolos Inovadores são Criados e Adotados em Escala Mundial: Uma visão sobre o Internet Engineering Task Force (IETF) e a Infraestrutura da Internet. In: WEBER, T. S.; MARTINS, C. A. (Ed.). Jornadas de Atualização em Informática 2020. Cuiabá, MT Brazil: Sociedade Brasileira de Computação, 2020. p. 45. ISBN 978-65-87003-28-3. Available in: https://doi.org/10.5753/sbc.5728.3.2.
BRAGA, J.; REGATEIRO, F.; DIAS, J. Machine Learning Ontology (MLOnto) Repository. 2020. Project Repository: OSF. DOI: 10.17605/OSF.IO/CHU5Q. Available in:
BRAGA, J.; SILVA, J. N.; ENDO, P.; OMAR, N. Structure for knowledge acquisition, use, learning and collaboration inter agents over internet infrastructure domains: Proceedings of the 2019 computing conference. In: ARAI, K.; BHATIA, R.; KAPOOR, S. (Ed.). Intelligent Computing. [S.l.]: Springer International Publishing, 2019. v. 1, p. 527–547. Doi: 10.1007/978-3-030-22871-2, ISBN: 978-3-030-22871-2.
BRANK, J.; GROBELNIK, M.; MLADENIC, D. A survey of ontology evaluation techniques. In: Proceedings of the conference on data mining and data warehouses (SiKDD 2005). Ljubljana, Slovenia: SiKDD, 2005. p. 166–170.
BUITELAAR, P.; CIMIANO, P.; MAGNINI, B. Ontology learning from text: An overview. Ontology learning from text: Methods, evaluation and applications, IOS Press, Amsterdam, v. 123, p. 3–12, 2005.
BUITELAAR, P.; OLEJNIK, D.; SINTEK, M. A protégé plug-in for ontology extraction from text based on linguistic analysis. In: SPRINGER. European Semantic Web Symposium. [S.l.], 2004. p. 31–44.
CARLSON, A.; BETTERIDGE, J.; KISIEL, B.; SETTLES, B.; HRUSCHKA, E. R.; MITCHELL, T. M. Toward an architecture for never-ending language learning. In: Twenty-Fourth AAAI Conference on Artificial Intelligence. Atlanta, US: AAAI Publications, 2010. p. 8.
CIMIANO, P.; VÖLKER, J. A framework for ontology learning and data-driven change discovery. In: SPRINGER. Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB). [S.l.], 2005. p. 227–238.
DOAN, A.; MADHAVAN, J.; DOMINGOS, P.; HALEVY, A. Ontology matching: A machine learning approach. In: Handbook on ontologies. [S.l.]: Springer, 2004. p. 385–403.
EHRIG, M. Ontology Alignment: Bridging the Semantic Gap. 1. ed. Germany: Springer, 2007.
FU, H.; CHI, Z.; FENG, D.; SONG, J. Machine learning techniques for ontology-based leaf classification. In: IEEE. ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004. [S.l.], 2004. v. 1, p. 681–686.
GOLBREICH, C.; DAMERON, O.; GIBAUD, B.; BURGUN, A. Web ontology language requirements wrt expressiveness of taxonomy and axioms in medicine. In: SPRINGER. International Semantic Web Conference. [S.l.], 2003. p. 180–194.
GRUBER, T. R. A translation approach to portable ontology specifications. Knowledge Acquisition, v. 5, p. 199–220, 1993.
GUARINO, N.; OBERLE, D.; STAAB, S. What is an ontology? In: Handbook on ontologies. [S.l.]: Springer, 2009. p. 1–17.
GUIZZARDI, G.; WAGNER, G.; ALMEIDA, J. P. A.; GUIZZARDI, R. S. Towards ontological foundations for conceptual modeling: The unified foundational ontology (ufo) story. Applied ontology, IOS Press, v. 10, n. 3-4, p. 259–271, 2015.
HITZLER, P.; KRöTZSCH, M.; PARSIA, B.; PATEL-SCHNEIDER, P. F.; RUDOLPH, S. OWL 2 Web Ontology Language Primer. W3C Recommendation, v. 27, n. 1, p. 1–123, 2009. Available in: https://www.w3.org/TR/owl2-primer/
HORRIDGE, M. A Practical Guide To Building OWL Ontologies Using Protégé 4 and CO-ODE Tools Edition 1.3. [S.l.], 2011. 108 p. Disponível em: https://www.researchgate.net/publication/230585369_A_Practical_Guide_To_Building_OWL_Ontologies_Using_The_Prot%27eg%27e-OWL_Plugin_and_CO-ODE_Tools.
ICHISE, R. Machine learning approach for ontology mapping using multiple concept similarity measures. In: IEEE. Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). [S.l.], 2008. p. 340–346.
KALIBATIENE, D.; VASILECAS, O. Survey on ontology languages. In: SPRINGER. International Conference on Business Informatics Research. [S.l.], 2011. p. 124–141.
KONYS, A. Knowledge systematization for ontology learning methods. Procedia computer science, Elsevier, v. 126, p. 2194–2207, 2018.
MITCHELL, T.; COHEN, W.; HRUSCHKA, E.; TALUKDAR, P.; YANG, B.; BETTERIDGE, J.; CARLSON, A.; DALVI, B.; GARDNER, M.; KISIEL, B. et al. Never-ending learning. Communications of the ACM, v. 61, n. 5, p. 103–115, 2018.
MUSEN, M. A. The protégé project: a look back and a look forward. AI Matters, v. 1, n. 4, p. 4–12, 2015. Disponível em: https://doi.org/10.1145/2757001.2757003.
PEASE, A. Ontology. [S.l.]: Articulate Software Press, 2011. ISBN: 1889455105. POON, H.; DOMINGOS, P. Unsupervised ontology induction from text. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics. [S.l.: s.n.], 2010. p. 296–305.
REN, F. A demo for constructing domain ontology from academic papers. In: Proceedings of COLING 2012: Demonstration Papers. [S.l.: s.n.], 2012. p. 369–376.
SACHA, D.; KRAUS, M.; KEIM, D. A.; CHEN, M. Vis4ml: An ontology for visual analytics assisted machine learning. IEEE transactions on visualization and computer graphics, IEEE, v. 25, n. 1, p. 385–395, 2018.
SHAMSFARD, M.; BARFOROUSH, A. A. Learning ontologies from natural language texts. International journal of human-computer studies, Elsevier, v. 60, n. 1, p. 17–63, 2004.
SHEKHAR, M.; K, S. R. Semantic Web Search based on Ontology Modeling using Protege Reasoner. 2013.
SILVER, D. L.; YANG, Q.; LI, L. Lifelong machine learning systems: Beyond learning algorithms. In: 2013 AAAI spring symposium series. [S.l.: s.n.], 2013.
SLIMANI, T. Ontology development: A comparing study on tools, languages and formalisms. Indian Journal of Science and Technology, v. 8, n. 24, p. 1–12, 2015.
STUDER, R.; BENJAMINS, V. R.; FENSEL, D. Knowledge engineering: principles and methods. Data & knowledge engineering, Elsevier, v. 25, n. 1-2, p. 161–197, 1998.
TAN, H.; LAMBRIX, P. Selecting an ontology for biomedical text mining. In: Proceedings of the BioNLP 2009 Workshop. [S.l.: s.n.], 2009. p. 55–62.
TUDORACHE, T.; NYULAS, C.; NOY, N. F.; MUSEN, M. A. Webprotégé: A collaborative ontology editor and knowledge acquisition tool for the web. Semantic web, IOS Press, v. 4, n. 1, p. 89–99, 2013.
XIAO, G.; DING, L.; COGREL, B.; CALVANESE, D. Virtual knowledge graphs: An overview of systems and use cases. Data Intelligence, MIT Press, v. 1, n. 3, p. 201–223, 2019.
XIAO, G.; LANTI, D.; KONTCHAKOV, R.; KOMLA-EBRI, S.; GÜZEL-KALAYCI, E.; DING, L.; CORMAN, J.; COGREL, B.; CALVANESE, D.; BOTOEVA, E. The virtual knowledge graph system ontop. In: SPRINGER. International Semantic Web Conference. [S.l.], 2020. p. 259–277.
Published
2021-07-18
How to Cite
BRAGA, Julião; REGATEIRO, Francisco; DIAS, Joaquim L. R.; STIUBIENER, Itana.
A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure. In: PRE-IETF WORKSHOP (WPIETF), 8. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1-12.
ISSN 2595-6388.
DOI: https://doi.org/10.5753/wpietf.2021.15778.
