Semantic Toronto Publish/Subscribe System

Title: Semantic Toronto Publish/Subscribe System

Research Question: How can semantic capabilities be added to existing publish/subscribe systems to improve the efficiency and accuracy of information dissemination?

Methodology: The researchers developed a semantic publish/subscribe system called S-ToPSS (Semantic Toronto Publish/Subscribe System). They used a knowledge representation model called OntoClean, which is designed to clean up and organize knowledge in a structured and semantically accurate way. They applied OntoClean to the domain of job recruitment to create an ontology that includes terms like "university," "degree," and "professional experience." They then designed a matching algorithm that can understand the semantics of these terms and match them appropriately.

Results: The researchers demonstrated that their semantic matching algorithm can handle complex queries and semantic ambiguities more effectively than existing algorithms. They showed that their system can accurately match events to subscriptions in the job recruitment domain, even when the exact terms used in the event and subscription are not the same.

Implications: The development of a semantic publish/subscribe system can significantly improve the efficiency and accuracy of information dissemination in many domains. By adding semantic capabilities to existing systems, users can subscribe to and receive information that is more relevant and precise to their needs. This can lead to better decision-making and more effective use of resources.

Link to Article: https://arxiv.org/abs/0311041v1 Authors: arXiv ID: 0311041v1