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''DSSim〔〔 is an ontology mapping system, that has been conceived to achieve a certain level of the envisioned machine intelligence on the Semantic Web. The main driving factors behind its development was to provide an alternative to the existing heuristics or machine learning based approaches with a multi-agent approach that makes use of uncertain reasoning. The system provides a possible approach to establish machine understanding over Semantic Web data through multi-agent beliefs and conflict resolution. ==Theoretical background== The DSSim framework for ontology mapping was introduced in 2005〔 by Miklos Nagy and Maria Vargas-Vera at the Open University (OU). DSSim addresses three challenges of the Semantic Web: * Uncertainty: Ontology mapping agents adopt the Dempster–Shafer theory for creating beliefs over mapping hypothesis. Based on evidences of similarity the mapping agents combine their beliefs in order to provide a coherent view on the mappings. The system is based on a theoretical mental model for software agents to represent beliefs over similarities of different terms in different ontologies. Through these beliefs that are derived using different similarity measure and background knowledge, each agent can establish certain understanding of the terms and their context. * Inconsistency: Conflicts in belief are resolved using fuzzy voting mechanism. Processing data on the Semantic Web produces scenarios where the different agents has conflicting beliefs over a particular solution. In these situations the agents need to resolve their conflicts in order to choose the best possible solution e.g. in our case mapping. Mapping agents use fuzzy voting to determine the best decision for agent society but in case voters make mistakes in their judgments, then the majority alternative (if it exists) is statistically most likely to be the best choice. The application of voting for mapping agents is a possible way to make systems more intelligent i.e. mimic the decision making how humans reach the decision on a problematic issue. * Vastness: Genetic algorithms based optimisations techniques are used in order to provide a reasonable time frame for belief combination using large ontologies. One of the main disadvantages of using Dempster-Shafer theory for uncertain reasoning is the computational complexity of the belief combination. DSSim resolves the problem by using genetic algorithm for creating the graphical structure that is used to compute the belief combination efficiently in the ontology mapping context. DSSim uses novel 3D visualisation techniques of both mapping and reasoning results. The main purpose of the reasoning storage and visualisation is to retain the reasoning states, in order to visualise it later to the end users. The main objective is to show to the end users why the system has selected a mapping candidate from two different ontologies. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「DSSim」の詳細全文を読む スポンサード リンク
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