@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{saidani_semantic-based_2020,

title = {A semantic-based classification approach for an enhanced spam detection},

author = {N. Saidani and K. Adi and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084283123&doi=10.1016%2fj.cose.2020.101716&partnerID=40&md5=539ac0fc0a7144fe983f514175a138e2},

doi = {10.1016/j.cose.2020.101716},

issn = {01674048 (ISSN)},

year  = {2020},

date = {2020-01-01},

journal = {Computers and Security},

volume = {94},

abstract = {In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020},

note = {Publisher: Elsevier Ltd},

keywords = {Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection},

pubstate = {published},

tppubtype = {article}

}