{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:34:15Z","timestamp":1760240055424,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Business information required for applications and business processes is extracted using systems like business rule engines. Since the advent of Big Data, such rule engines are producing rules in a big quantity whereas more rules lead to more complexity in semantic analysis and understanding. This paper introduces a method to handle semantic complexity in rules and support automated generation of Resource Description Framework (RDF) metadata model of rules and such model is used to assist in querying and analysing Big Data. Practically, the dynamic changes in rules can be a source of conflict in rules stored in a repository. It is identified during the literature review that there is a need of a method that can semantically analyse rules and help business analysts in testing and validating the rules once a change is made in a rule. This paper presents a robust method that not only supports semantic analysis of rules but also generates RDF metadata model of rules and provide support of querying for the sake of semantic interpretation of the rules. The results of the experiments manifest that consistency checking of a set of big data rules is possible through automated tools.<\/jats:p>","DOI":"10.3390\/sym11030309","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:45:36Z","timestamp":1551678336000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Handling Semantic Complexity of Big Data using Machine Learning and RDF Ontology Model"],"prefix":"10.3390","volume":"11","author":[{"given":"Rauf","family":"Sajjad","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5161-6441","authenticated-orcid":false,"given":"Imran Sarwar","family":"Bajwa","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan"}]},{"given":"Rafaqut","family":"Kazmi","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; IT, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tadikonda, V., and Rosca, D. (2016, January 1\u20133). Informed and Timely Business Decisions-A Data-driven Approach. Proceedings of the SEKE, San Francisco, CA, USA.","DOI":"10.18293\/SEKE2016-216"},{"key":"ref_2","unstructured":"OMG (2018, July 11). Semantics of Business Vocabulary and Business Rules (SBVR), Version 1.4. Available online: https:\/\/www.omg.org\/spec\/SBVR."},{"key":"ref_3","unstructured":"Bajwa, I.S., Lee, M.G., and Bordbar, B. (2011, January 23). SBVR Business Rules Generation from Natural Language Specification. Proceedings of the AAAI Spring Symposium: AI for Business Agility, Palo Alto, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1080\/07421222.2018.1451951","article-title":"Creating Strategic Value from Big Data Analytics: A Research Framework","volume":"35","author":"Grover","year":"2018","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_5","unstructured":"Carroll, J., Herman, I., and Patel-Schneider, P.F. OWL 2 Web Ontology Language RDF-Based Semantics, Available online: https:\/\/www.w3.org\/TR\/owl2-rdf-based-semantics\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.14778\/2824032.2824124","article-title":"Query-oriented summarization of RDF graphs","volume":"8","author":"Manolescu","year":"2015","journal-title":"Proc. VLDB Endow."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ceravolo, P., Fugazza, C., and Leida, M. (2007, January 21\u201323). Modeling semantics of business rules. Proceedings of the Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, 2007 (DEST\u201907), Cairns, Australia.","DOI":"10.1109\/DEST.2007.371965"},{"key":"ref_8","first-page":"724196","article-title":"A Novel Way to Relate Ontology Classes","volume":"1","author":"Choksi","year":"2015","journal-title":"Sci. World J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0169-023X(02)00195-7","article-title":"Methodologies, tools and languages for building ontologies. Where is their meeting point?","volume":"46","author":"Corcho","year":"2003","journal-title":"Data Knowl. Eng."},{"key":"ref_10","first-page":"540","article-title":"Transferring naive bayes classifiers for text classification","volume":"Volume 22","author":"Dai","year":"1999","journal-title":"Proceedings of the National Conference on Artificial Intelligence"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5755","DOI":"10.1016\/j.eswa.2013.04.023","article-title":"Assessing sentence scoring techniques for extractive text summarization","volume":"40","author":"Ferreira","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_12","unstructured":"Jena.apache.org (2018, March 01). Apache Jena\u2014Jena Ontology API. N.p. Available online: https:\/\/jena.apache.org\/documentation\/ontology\/."},{"key":"ref_13","unstructured":"Krieger, H.U. (2014, January 26). A Detailed Comparison of Seven Approaches for the Annotation of Time-Dependent Factual Knowledge in RDF and OWL. Proceedings of the 10th Joint ISO-ACL SIGSEM Workshop on Interoperable Semantic Annotation, Reykjavik, Iceland."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liang, P., and Klein, D. (June, January 31). Online EM for unsupervised models. Proceedings of the Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, CO, USA.","DOI":"10.3115\/1620754.1620843"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TKDE.2013.19","article-title":"A similarity measure for text classification and clustering","volume":"26","author":"Lin","year":"2014","journal-title":"Knowl. Data Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, H.T., Bui, N., and Honavar, V. (November, January 29). Learning classifiers from remote RDF data stores augmented with RDFS subclass hierarchies. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7363953"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007692713085","article-title":"Text classification from labelled and unlabelled documents using EM","volume":"39","author":"Nigam","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_18","unstructured":"Nigam, K., McCallum, A., and Mitchell, T. (2019, February 19). Semi-Supervised Text Classification Using EM. Semi-Supervised Learning. Available online: http:\/\/parnec.nuaa.edu.cn\/seminar\/2012_Spring\/20120323\/%E8%92%8B%E8%90%8D\/Semi-Supervised%20Text%20Classification%20Using%20EM.pdf."},{"key":"ref_19","unstructured":"Paik, J.H. (August, January 28). A novel TF-IDF weighting scheme for effective ranking. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saggion, H., Funk, A., Maynard, D., and Bontcheva, K. (2007, January 11\u201315). Ontology-based information extraction for business intelligence. Proceedings of the 6th International Conference on Semantic Web, Busan, Korea.","DOI":"10.1007\/978-3-540-76298-0_61"},{"key":"ref_21","unstructured":"(2015, February 15). Scikit-learn.org. 4.1. Feature Extraction Scikit-Learn 0.15.2 Documentation. Available online: http:\/\/scikit-learn.org\/stable\/modules\/feature_extraction.html#feature-extraction."},{"key":"ref_22","unstructured":"Baudel, T., and Frank, V. (2014). Rule correlation to rules input attributes according to disparate distribution analysis. (No. 88,25,588), U.S. Patent."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guiss\u00e9, A., L\u00e9vy, F., and Nazarenko, A. (2012, January 27\u201329). From regulatory texts to BRMS: How to guide the acquisition of business rules?. Proceedings of the International Workshop on Rules and Rule Markup Languages for the Semantic Web, Montpellier, France.","DOI":"10.1007\/978-3-642-32689-9_7"},{"key":"ref_24","first-page":"4456","article-title":"A Novel Proposal for Bridging Gap between RDB-RDF Semantic Web using Bidirectional Approach","volume":"11","author":"Sitharamulu","year":"2016","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Paulheim, H., Plendl, R., Probst, F., and Oberle, D. (2011, January 11\u201316). Mapping pragmatic class models to reference ontologies. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops (ICDEW), Hannover, Germany.","DOI":"10.1109\/ICDEW.2011.5767660"},{"key":"ref_26","unstructured":"Lu, R., and Sadiq, S. (2007, January 25\u201327). A survey of comparative business process modeling approaches. Proceedings of the International Conference on Business Information Systems, Poznan, Poland."},{"key":"ref_27","unstructured":"Cimiano, P., Haase, P., Herold, M., Mantel, M., and Buitelaar, P. (2007, January 11\u201315). Lexonto: A model for ontology lexicons for ontology-based NLP. Proceedings of the OntoLex07 Workshop Held in Conjunction with ISWC\u201907, Busan, Korea."},{"key":"ref_28","unstructured":"(2015, January 30). W3.org. RDF Schema 1.1. Available online: http:\/\/www.w3.org\/TR\/2014\/PER-rdf-schema-20140109\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MIS.2003.1179189","article-title":"Automatic ontology-based knowledge extraction from web documents","volume":"18","author":"Alani","year":"2003","journal-title":"Intell. Syst. IEEE"},{"key":"ref_30","unstructured":"(2018, August 13). SRS for Cafeteria Ordering System\u2014Seidenberg School of... (n.d.). Available online: http:\/\/csis.pace.edu\/~marchese\/SE616_New\/Samples\/SE616_SRS.doc."},{"key":"ref_31","first-page":"12","article-title":"A Step towards Ambiguity less Natural Language Software Requirements Specifications","volume":"4","author":"Umber","year":"2012","journal-title":"Int. J. Web Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:35Z","timestamp":1760186135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,1]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["sym11030309"],"URL":"https:\/\/doi.org\/10.3390\/sym11030309","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,3,1]]}}}