Constructing and mapping fuzzy thematic clusters to higher ranks in a taxonomy
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
A hybrid cluster-lift method for the analysis of research activities
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
Similarity measures are mechanisms that assign a numeric score indicating how closely two documents, or a document and a query match. Most similarity measures such as Cosine measure, which treat a document as a vector of weighted keywords, consider exact matching of keywords when determining the similarity among documents and they do not consider the semantic similarity among the keywords of the documents. This paper presents a Category-based Similarity Algorithm (CSA) to determine the semantic similarity between any two pieces of information. CSA is implemented inside the ACORN (Agent-based Community Oriented Routing Network) system, which is a multi-agent system for information retrieval and provision in a community of users. CSA can also be used in any information sharing system in which the information content is represented as vectors of weighted keywords.