{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:35:46Z","timestamp":1760240146409,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T00:00:00Z","timestamp":1551744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006378","name":"Universitas Indonesia","doi-asserted-by":"publisher","award":["1266\/UN2.R3.1\/HKP.05.00\/ 2018"],"award-info":[{"award-number":["1266\/UN2.R3.1\/HKP.05.00\/ 2018"]}],"id":[{"id":"10.13039\/501100006378","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.<\/jats:p>","DOI":"10.3390\/info10030095","type":"journal-article","created":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T11:19:50Z","timestamp":1551784790000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["ByNowLife: A Novel Framework for OWL and Bayesian Network Integration"],"prefix":"10.3390","volume":"10","author":[{"given":"Foni A.","family":"Setiawan","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia"},{"name":"Research Center for Limnology, Indonesian Institute of Sciences, Jakarta 12710, Indonesia"}]},{"given":"Eko K.","family":"Budiardjo","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia"}]},{"given":"Wahyu C.","family":"Wibowo","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,5]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider, P.F. (2007). OWL: A Description Logic Based Ontology Language for the Semantic Web. The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press.","key":"ref_1","DOI":"10.1017\/CBO9780511711787"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.ijar.2013.09.005","article-title":"Inconsistency-Tolerant Reasoning with OWL DL","volume":"55","author":"Zhang","year":"2014","journal-title":"Int. J. Approx. Reason."},{"doi-asserted-by":"crossref","unstructured":"Setiawan, F.A., Budiardjo, E.K., Basaruddin, T., and Aminah, S. (2017). A Systematic Literature Review on Combining Ontology with Bayesian Network to Support Logical and Probabilistic Reasoning. Proceedings of the 2017 International Conference on Software and e-Business\u2014ICSEB 2017, ACM Press.","key":"ref_3","DOI":"10.1145\/3178212.3178223"},{"doi-asserted-by":"crossref","unstructured":"Apicella, A., Corazza, A., Isgr\u00f2, F., and Vettigli, G. (2018). Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification. Information, 9.","key":"ref_4","DOI":"10.3390\/info9100252"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-319-30528-8_3","article-title":"Construction and Restructuring of the Knowledge Repository of Website Evaluation Methods","volume":"Volume 243","author":"Ziemba","year":"2016","journal-title":"Lecture Notes in Business Information Processing"},{"unstructured":"Haberlin, R., da Costa, P.C.G., and Laskey, K.B. Probabilistic Ontology Architecture for a Terrorist Identification Decision Support System. 19th International Command and Control Research and Technology Symposium, 16\u201319 June 2014, Command and Control Research Program (U.S.).","key":"ref_6"},{"unstructured":"(2018, December 09). OWL Web Ontology Language Guide. Available online: https:\/\/www.w3.org\/TR\/owl-guide\/#OwlVarieties.","key":"ref_7"},{"key":"ref_8","first-page":"539","article-title":"Handling Uncertainty in Ontology Construction Based on Bayesian Approaches: A Comparative Study","volume":"Volume 516","author":"Intan","year":"2015","journal-title":"4th International Conference on Soft Computing, Intelligent Systems, and Information Technology"},{"unstructured":"Zhang, S., Sun, Y., Peng, Y., and Wang, X. (2009). BayesOWL: A Prototype System for Uncertainty in Semantic Web. Proceedings of the 2009 International Conference on Artificial Intelligence, 13\u201316 July, IC-AI.","key":"ref_9"},{"unstructured":"Ding, Z., and Peng, Y. (, January 15\u201318). A Bayesian Approach to Uncertainty Modelling in OWL Ontology. Proceedings of the International Conference on Advances in Intelligent Systems -Theory and Applications, Centre de Recherche Public Henri Tudor, Luxembourg-Kirchberg, Luxembourg.","key":"ref_10"},{"doi-asserted-by":"crossref","unstructured":"Carvalho, R.N., Costa, P.C.G., Laskey, K.B., and Chang, K.C. (2010, January 26\u201329). PROGNOS: Predictive situational awareness with probabilistic ontologies. Proceedings of the 2010 13th International Conference on Information Fusion, Edinburgh, UK.","key":"ref_11","DOI":"10.1109\/ICIF.2010.5711970"},{"doi-asserted-by":"crossref","unstructured":"Boruah, A., and Hazarika, S.M. (2014, January 20\u201321). An MEBN Framework as a dynamic firewall\u2019s knowledge flow architecture. Proceedings of the International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","key":"ref_12","DOI":"10.1109\/SPIN.2014.6776957"},{"key":"ref_13","first-page":"147","article-title":"Probabilistic ontologies for Knowledge Fusion","volume":"213","author":"Laskey","year":"2010","journal-title":"Front. Artif. Intell. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.artint.2007.09.006","article-title":"MEBN: A language for first-order Bayesian knowledge bases","volume":"172","author":"Laskey","year":"2008","journal-title":"Artif. Intell."},{"key":"ref_15","first-page":"36","article-title":"A new algorithm for Generating Situation-Specific Bayesian Networks Using Bayes-Ball Method","volume":"Volume 1665","author":"Santos","year":"2016","journal-title":"CEUR Workshop Proceedings"},{"unstructured":"Yang, Y. (2007). A Framework for Decision Support Systems Adapted to Uncertain Knowledge. [Ph.D. Thesis, der Universit\u00e4t at Fridericiana zu Karlsruhe (TH)].","key":"ref_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40064-016-2331-1","article-title":"Knowledge-oriented semantics modelling towards uncertainty reasoning","volume":"5","author":"Mohammed","year":"2016","journal-title":"Springerplus"},{"doi-asserted-by":"crossref","unstructured":"Fenz, S., Tjoa, A.M., and Hudec, M. (2009). Ontology-based generation of bayesian networks. Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009, IEEE.","key":"ref_18","DOI":"10.1109\/CISIS.2009.33"},{"key":"ref_19","first-page":"9","article-title":"Ben Ontology-based generation of Object Oriented Bayesian Networks","volume":"Volume 818","author":"Ishak","year":"2011","journal-title":"CEUR Workshop Proceedings"},{"unstructured":"(2017, March 30). BayesFusion Appendices: XDSL File Format\u2014XML Schema Definitions. Available online: https:\/\/dslpitt.org\/genie\/download\/xdsl_schema.zip.","key":"ref_20"},{"unstructured":"(2018, December 09). ByNowLife\u2014Bayesian Network and OWL Integration Framework. Available online: http:\/\/www.bynowlife.net.","key":"ref_21"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1016\/j.procs.2015.05.378","article-title":"An Individual-Centric Probabilistic Extension for OWL: Modelling the Uncertainness","volume":"Volume 51","author":"Pileggi","year":"2015","journal-title":"Procedia\u2014Procedia Computer Science"},{"unstructured":"(2018, December 09). BayesFusion Downloads for Academia. Available online: https:\/\/download.bayesfusion.com\/files.html?category=Academia.","key":"ref_23"},{"unstructured":"(2018, December 09). Universitas Ibn Khaldun Bogor. Available online: http:\/\/www.uika-bogor.ac.id.","key":"ref_24"},{"key":"ref_25","first-page":"131","article-title":"The Critical Success Factors for Customer Relationship Management Implementation: a Systematic Literature Review","volume":"23","author":"Meyliana","year":"2016","journal-title":"Int. J. Bus. Inf. Syst."},{"doi-asserted-by":"crossref","unstructured":"Pearl, J. (1988). Markov Networks. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers Inc.","key":"ref_26","DOI":"10.1016\/B978-0-08-051489-5.50008-4"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/3\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:36:27Z","timestamp":1760186187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/3\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,5]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["info10030095"],"URL":"https:\/\/doi.org\/10.3390\/info10030095","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2019,3,5]]}}}