Christos Rodosthenous

Ph.D, M.Sc., B.Sc.

Publications

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154 2014 4230 academic repository and phrases story understanding argumentation asynchronous cloze tests cmn collaboration Commonsense Knowledge commonsense knowledge acquisition Computer Assisted Language Learning ConceptNet crowdsourcing digital literacy digital object identifier 10 digitization distance education distance education tools e-learning elearning eLearning Platform enetCollect Environmental Education evaluation experiences in supporting elearning tool games Games With A Purpose Gamification Geographic Focus Identification Geographic Information Systems Human computer interaction immersive technologies in-service teachers Information Retrieval infrastructure IoT Knowledge Bases knowledge engineering knowledge representation Language learning Language Resources learning lecture capture Lexicon library LMS Moodle Natural Language Processing oasics open data participatory design plagiarism detection reasoning STEAM education story understanding student assessment synchronous teaching training program university students user engagement video visual programming web-based ide

2018
	Rodosthenous, Christos; Michael, Loizos GeoMantis: Inferring the Geographic Focus of Text using Knowledge Bases Proceedings Article In: Rocha, Ana Paula; van den Herik, Jaap (Ed.): Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART2018), 2018. Abstract \| BibTeX \| Tags: Geographic Focus Identification, Geographic Information Systems, Information Retrieval, Knowledge Bases, Natural Language Processing \| Links: @inproceedings{Rodosthenous2018, title = {GeoMantis: Inferring the Geographic Focus of Text using Knowledge Bases}, author = {Christos Rodosthenous and Loizos Michael}, editor = {Ana Paula Rocha and Jaap van den Herik}, url = {https://www.christosrodosthenous.info/wp-content/uploads/2018/02/ICAART_2018_50_CR.pdf http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=hDHdIxIPFBU=&t=1}, year = {2018}, date = {2018-01-17}, booktitle = {Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART2018)}, series = {10th International Conference on Agents and Artificial Intelligence (ICAART18)}, abstract = {We consider the problem of identifying the geographic focus of a document. Unlike some previous work on this problem, we do not expect the document to explicitly mention the target region, making our problem one of inference or prediction, rather than one of identification. Further, we seek to tackle the problem without appealing to specialized geographic information resources like gazetteers or atlases, but employ general-purpose knowledge bases and ontologies like ConceptNet and YAGO. We propose certain natural strategies towards addressing the problem, and show that the GeoMantis system that implements these strategies outperforms an existing state-of-the-art system, when compared on documents whose target region (country, in particular) is not explicitly mentioned or is obscured. Our results give evidence that using general-purpose knowledge bases and ontologies can, in certain cases, outperform even specialized tools.}, keywords = {Geographic Focus Identification, Geographic Information Systems, Information Retrieval, Knowledge Bases, Natural Language Processing}, pubstate = {published}, tppubtype = {inproceedings} } Close We consider the problem of identifying the geographic focus of a document. Unlike some previous work on this problem, we do not expect the document to explicitly mention the target region, making our problem one of inference or prediction, rather than one of identification. Further, we seek to tackle the problem without appealing to specialized geographic information resources like gazetteers or atlases, but employ general-purpose knowledge bases and ontologies like ConceptNet and YAGO. We propose certain natural strategies towards addressing the problem, and show that the GeoMantis system that implements these strategies outperforms an existing state-of-the-art system, when compared on documents whose target region (country, in particular) is not explicitly mentioned or is obscured. Our results give evidence that using general-purpose knowledge bases and ontologies can, in certain cases, outperform even specialized tools. Close
2017
	Rodosthenous, Christos; Michael, Loizos Inferring the Geographic Focus of Stories Using Crowdsourced Knowledge Bases Workshop Cognition and Artificial Intelligence for Human-Centred Design (CAID2017), 2017. Abstract \| BibTeX \| Tags: Geographic Focus Identification, Geographic Information Systems, Information Retrieval, Knowledge Bases, Natural Language Processing @workshop{Rodosthenous2018b, title = {Inferring the Geographic Focus of Stories Using Crowdsourced Knowledge Bases}, author = {Christos Rodosthenous and Loizos Michael}, year = {2017}, date = {2017-08-19}, booktitle = {Cognition and Artificial Intelligence for Human-Centred Design (CAID2017)}, abstract = {We consider the problem of identifying the geographic focus of a story and more specifically a news story. Most of the times, we do not expect the story to explicitly mention the target region, making our problem one of inference or prediction, rather than one of identification. Further, we seek to tackle the problem without appealing to specialized geographic information resources like gazetteers or atlases, but employ only general-purpose crowdsourced knowledge bases and ontologies like ConceptNet and YAGO and techniques that are cognitively compatible with human reasoning. In particular, we propose certain natural strategies towards addressing the problem, and show that the GeoMantis system that implements these strategies outperforms an existing state-of-the-art system, when compared on stories whose target region (country, in particular) is not explicitly mentioned or is obscured. Our results give evidence that using general-purpose crowdsourced knowledge bases and ontologies can, in certain cases, outperform even specialized tools.}, keywords = {Geographic Focus Identification, Geographic Information Systems, Information Retrieval, Knowledge Bases, Natural Language Processing}, pubstate = {published}, tppubtype = {workshop} } Close We consider the problem of identifying the geographic focus of a story and more specifically a news story. Most of the times, we do not expect the story to explicitly mention the target region, making our problem one of inference or prediction, rather than one of identification. Further, we seek to tackle the problem without appealing to specialized geographic information resources like gazetteers or atlases, but employ only general-purpose crowdsourced knowledge bases and ontologies like ConceptNet and YAGO and techniques that are cognitively compatible with human reasoning. In particular, we propose certain natural strategies towards addressing the problem, and show that the GeoMantis system that implements these strategies outperforms an existing state-of-the-art system, when compared on stories whose target region (country, in particular) is not explicitly mentioned or is obscured. Our results give evidence that using general-purpose crowdsourced knowledge bases and ontologies can, in certain cases, outperform even specialized tools. Close