@article{azzi_scoring_2023,

title = {Scoring Ontologies for Reuse: An Approach for Fitting Semantic Requirements},

author = {S. Azzi and A. Assi and S. Gagnon},

editor = {Vlachidis A. Garoufallou E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172082147&doi=10.1007%2f978-3-031-39141-5_17&partnerID=40&md5=f2d307e9185aca3ae5a69c31e8e4440a},

doi = {10.1007/978-3-031-39141-5_17},

issn = {18650929},

year  = {2023},

date = {2023-01-01},

journal = {Communications in Computer and Information Science},

volume = {1789 CCIS},

pages = {203–208},

abstract = {The process of reusing ontologies is still challenging for the ontological community. One of the challenging efforts is to select the most relevant ontology from a set of candidates that needs a deep consideration. After the step of finding the candidates, many of them can be more appropriate than others as they fit better to the ontology requirements expressed by competency questions. First, we develop a mathematical formalisation based on Set Theory and we design the problem as an optimization problem to assist the knowledge engineer in selecting ontologies. Then, we provide formal steps to make well-founded comparison across a set of candidate ontologies. At last, we propose metrics to quantify the decision during the selection step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {ISBN: 9783031391408 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {Competency question, Fitting semantics, Formalisation, Ontological communities, Ontology, Ontology engineering, Ontology reuse, Ontology's, PNADO, Reuse, Semantic requirement, Semantic Web, Semantics},

pubstate = {published},

tppubtype = {article}

}

@article{azzi_ontology-driven_2023,

title = {Ontology-Driven Parliamentary Analytics: Analysing Political Debates on COVID-19 Impact in Canada},

author = {S. Azzi and S. Gagnon},

editor = {Asemi A. Francesconi E. Ko A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172088757&doi=10.1007%2f978-3-031-39841-4_7&partnerID=40&md5=5dff3b672c36c66070042a35eed048e0},

doi = {10.1007/978-3-031-39841-4_7},

issn = {03029743},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {14149 LNCS},

pages = {89–102},

abstract = {Parliamentary debates are usually published in Parliament’s websites to allows citizens to be informed on the latest national debates, proposals and decisions. To enhance citizen experience and engagement, functionalities such as debates annotation and question answering are necessary. Annotating text requires semantic content and ontologies are known for their ability to describe a common vocabulary for a domain and can be a solid base for annotation and question answering. We report on an ongoing study to enhance parliamentary analytics using an ontology and knowledge graph to sharpen annotations and facilitate their query by end-users. As a salient case, a sample of debates are collected on the COVID-19 impact in Canada, as its complexity shows the relevance of using advanced knowledge representation techniques. We focused on the development of a new “Impact of COVID-19 in Canada Ontology” (ICCO) that provides contextualized semantic information on impact in numerous policy areas, as this ontology is entirely built from Canadian parliamentary debates. It has been evaluated and validated by experts. Our conclusion underscores the importance of integrating ontology-driven parliamentary analytics within the broader context of digital transformation in legislative institutions, and the need for new platforms supporting free and open Digital Humanities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {ISBN: 9783031398407 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {COVID-19, End-users, Knowledge graph, Knowledge graphs, Ontology, Ontology graphs, Ontology's, Parliamentary debate, Political debates, Question Answering, Semantic content, Semantic ontology, Semantics, Solid basis},

pubstate = {published},

tppubtype = {article}

}

@article{gagnon_automated_2011,

title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1)},

author = {S. Gagnon and S. Messaoudi and A. Charbonneau},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},

issn = {978-235768024-1 (ISBN)},

year  = {2011},

date = {2011-01-01},

booktitle = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},

journal = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},

pages = {151–158},

address = {Avignon},

abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},

keywords = {Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing},

pubstate = {published},

tppubtype = {article}

}