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Dr Kit-ying Hui's Outputs (6)

Taxonomic corpus-based concept summary generation for document annotation.
Presentation / Conference Contribution
NKISI-ORJI, I., WIRATUNGA, N., HUI, K.-Y., HEAVEN, R. and MASSIE, S. 2017. Taxonomic corpus-based concept summary generation for document annotation. In Kampus, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L. and Karydis, I. (eds.) Proceedings of the 21st International conference on theory and practice of digital libraries (TPDL 2017): research and advanced technology for digital libraries, 18-21 September 2017, Thessaloniki, Greece. Lecture notes in computer science, 10450. Cham: Springer [online], pages 49-60. Available from: https://doi.org/10.1007/978-3-319-67008-9_5

Semantic annotation is an enabling technology which links documents to concepts that unambiguously describe their content. Annotation improves access to document contents for both humans and software agents. However, the annotation process is a chall... Read More about Taxonomic corpus-based concept summary generation for document annotation..

A hybrid approach to distributed constraint satisfaction.
Presentation / Conference Contribution
LEE, D., ARANA, I., AHRIZ, H. and HUI, K.-Y. 2008. A hybrid approach to distributed constraint satisfaction. In Dochev, D., Pistore, M. and Traverso, P. (eds.) Proceedings of the 13th International conference on artificial intelligence: methodology, systems and applications (AIMSA 2008), 4-6 September 2008, Varna, Bulgaria. Lecture notes in computer science, 5253. Berlin: Springer [online], pages 375-379. Available from: https://doi.org/10.1007/978-3-540-85776-1_33

We present a hybrid approach to Distributed Constraint Satisfaction which combines incomplete, fast, penalty-based local search with complete, slower systematic search. Thus, we propose the hybrid algorithm PenDHyb where the distributed local search... Read More about A hybrid approach to distributed constraint satisfaction..

A hybrid approach to solving coarse-grained DisCSPs.
Presentation / Conference Contribution
LEE, D., ARANA, I., AHRIZ, H. and HUI, K.-Y. 2009. A hybrid approach to solving coarse-grained DisCSPs. In Proceedings of the 8th International conference on autonomous agents and multiagent systems (AAMAS 2009), 10-15 May 2009, Budapest, Hungary. Richland, South Carolina: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) [online], pages 1235-1236. Available from: http://www.aamas-conference.org/Proceedings/aamas09/pdf/02_Extended_Abstract/C_SP_0555.pdf

A coarse-grained Distributed Constraint Satisfaction Problem (DisCSP) consists of several loosely connected constraint satisfaction subproblems, each assigned to an individual agent. We present Multi-Hyb, a two-phase concurrent hybrid approach for so... Read More about A hybrid approach to solving coarse-grained DisCSPs..

Multi-Hyb: a hybrid algorithm for solving DisCSPs with complex local problems.
Presentation / Conference Contribution
LEE, D., ARANA, I., AHRIZ, H. and HUI, K.-Y. 2009. Multi-Hyb: a hybrid algorithm for solving DisCSPs with complex local problems. In Baeza-Yates, R., Lang, J., Mitra, S., Parsons, S. and Pasi, G. (eds.) Proceedings of the 2009 IEEE/WIC/ACM international conference on intelligent agent technology (IAT 2009), co-located with the 2009 IEEE/WIC/ACM international conference on web intelligence (WI 2009), and the joint conference workshops (WI-IAT Workshops 2009), 15-18 September 2009, Milan, Italy. Los Alamitos: IEEE Computer Society [online], volume 2, article number 5284811, pages 379-382. Available from: https://doi.org/10.1109/WI-IAT.2009.181

A coarse-grained Distributed Constraint Satisfaction Problem (DisCSP) is a constraint problem where several agents, each responsible for solving one part (a complex local problem), cooperate to determine an overall solution. Thus, agents solve the ov... Read More about Multi-Hyb: a hybrid algorithm for solving DisCSPs with complex local problems..

Multi-HDCS: solving DisCSPs with complex local problems cooperatively.
Presentation / Conference Contribution
LEE, D., ARANA, I., AHRIZ, H. and HUI, K. 2009. Multi-HDCS: solving DisCSPs with complex local problems cooperatively. In Huang, X.J., Ghorbani, A.A., Hacid, M.-S. and Yamaguchi, T. (eds.) Proceedings of the 2010 IEEE/WIC/ACM international conference on intelligent agent technology (IAT 2010), co-located with the 2010 IEEE/WIC/ACM international conference on web intelligence (WI 2010), and the joint conference workshops (WI-IAT Workshops 2010), 31 August - 3 September 2010, Toronto, Canada. Los Alamitos: IEEE Computer Society [online], volume 2, article number 5614767, pages 295-302. Available from: https://doi.org/10.1109/WI-IAT.2010.141

We propose Multi-HDCS, a new hybrid approach for solving Distributed CSPs with complex local problems. In Multi-HDCS, each agent concurrently: (i) runs a centralised systematic search for its complex local problem; (ii) participates in a distributed... Read More about Multi-HDCS: solving DisCSPs with complex local problems cooperatively..

Ontology alignment based on word embedding and random forest classification.
Presentation / Conference Contribution
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..