Skip to main content

Research Repository

Advanced Search

Professor Nirmalie Wiratunga


Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. (2019)
Journal Article
CHEN, Y.Y., WIRATUNGA, N. and LOTHIAN, R. 2020. Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. Online information review [online], 44(2), pages 399-416. Available from: https://doi.org/10.1108/OIR-02-2017-0066

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-ge... Read More about Integrating selection-based aspect sentiment and preference knowledge for social recommender systems..

Developing a catalogue of explainability methods to support expert and non-expert users. (2019)
Conference Proceeding
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2019. Developing a catalogue of explainability methods to support expert and non-expert users. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXVI: proceedings of the 39th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) international Artificial intelligence conference 2019 (AI 2019), 17-19 December 2019, Cambridge, UK. Lecture notes in computer science, 11927. Cham: Springer [online], pages 309-324. Available from: https://doi.org/10.1007/978-3-030-34885-4_24

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design... Read More about Developing a catalogue of explainability methods to support expert and non-expert users..

Learning to self-manage by intelligent monitoring, prediction and intervention. (2019)
Conference Proceeding
WIRATUNGA, N., CORSAR, D., MARTIN, K., WIJEKOON, A., ELYAN, E., COOPER, K., IBRAHIM, Z., CELIKTUTAN, O., DOBSON, R.J., MCKENNA, S., MORRIS, J., WALLER, A., ABD-ALHAMMED, R., QAHWAJI, R. and CHAUDHURI, R. 2019. Learning to self-manage by intelligent monitoring, prediction and intervention. In Wiratunga, N., Coenen, F. and Sani, S. (eds.) Proceedings of the 4th International workshop on knowledge discovery in healthcare data (KDH 2019), co-located with the 28th International joint conference on artificial intelligence (IJCAI-19), 10-11 August 2019, Macao, China. CEUR workshop proceedings, 2429. Aachen: CEUR Workshop Proceedings [online], pages 60-67. Available from: http://ceur-ws.org/Vol-2429/paper10.pdf

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of out-of-hospital care requires researchers to expand their horizons; integrated assistive technologies sho... Read More about Learning to self-manage by intelligent monitoring, prediction and intervention..

Representation and learning schemes for argument stance mining. (2019)
Thesis
CLOS, J. 2019. Representation and learning schemes for argument stance mining. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Argumentation is a key part of human interaction. Used introspectively, it searches for the truth, by laying down argument for and against positions. As a mediation tool, it can be used to search for compromise between multiple human agents. For this... Read More about Representation and learning schemes for argument stance mining..

Aspect-based sentiment analysis for social recommender systems. (2019)
Thesis
CHEN, Y.Y. 2019. Aspect-based sentiment analysis for social recommender systems. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To e... Read More about Aspect-based sentiment analysis for social recommender systems..

Ontology driven information retrieval. (2019)
Thesis
NKISI-ORJI, I. 2019. Ontology driven information retrieval. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Ontology-driven information retrieval deals with the use of entities specified in domain ontologies to enhance search and browse. The entities or concepts of lightweight ontological resources are traditionally used to index resources in specialised d... Read More about Ontology driven information retrieval..

Ontology alignment based on word embedding and random forest classification. (2019)
Conference Proceeding
NKISI-ORJI, I., WIRATUNGA, N., MASSIE, S., HUI, K.-Y. and HEAVEN, R. 2019. Ontology alignment based on word embedding and random forest classification. In Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. and Ifrim, G. (eds.) Machine learning and knowledge discovery in databases: proceedings of the 2018 European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD 2018), 10-14 September 2018, Dublin, Ireland. Lecture notes in computer science, 11051. Cham: Springer [online], part I, pages 557-572. Available from: https://doi.org/10.1007/978-3-030-10925-7_34

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering corr... Read More about Ontology alignment based on word embedding and random forest classification..