Auto-Adaptive Systems for Monitoring and Updating Dynamic Knowledge Graphs

用于监控和更新动态知识图的自适应系统

• 批准号: 483298-2015
• 负责人: Wang, Chun
• 金额: $ 1.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=583451
• 关键词: Auto; Adaptive; Systems; Monitoring; Updating

Given the extraordinary volumes of fast moving data on the Internet, users need cognitive services to help them make complex decisions based on the best available knowledge. In addition, these services should continually acquire new knowledge from the data fed into the system by mining data for information. The cognitive ability of a electronic commerce system relies on its global dynamic ontology which can be represented by dynamic knowledge graphs. In this research project, we propose a multi-agent system for effectively monitoring and updating large dynamic knowledge graphs of a semantic eCommerce search engine "daneel", which has been developed by Azzimov Inc. The proposed system will be used as an algorithmic tool to categorize eCommerce servers of Azzimov's interest on the Internet and optimize the graph for efficient search. One of the challenges in building such a tool is to calculate the chromatic number of a large scale, dynamic graph in a near real time manner. While calculating chromatic number of a graph is a combinatorial optimization problem that has been well studied in graph theory, however, existing approaches do not satisfy Azzimov's requirement of computing chromatic numbers of very large graphs with near real time responsiveness in a distributed and dynamic environment, such as the Internet. With the proposed multi-agent system for computing chromatic numbers, Azzimov's cognitive service system can monitor and updates its dynamic knowledge graphs with better speed and accuracy which enables better search results. The multi-agent system is designed based on the Auto-Adaptive Systems (AAS) architecture. The design of decision making modules of the agents and the interaction protocol between them are based on a distributed graph colouring algorithm which will also be developed in this project.

鉴于互联网上快速移动的数据量巨大，用户需要认知服务来帮助他们 他们根据现有的最佳知识作出复杂的决定。此外，这些服务应 通过从数据中挖掘信息，不断从输入系统的数据中获取新知识。的 电子商务系统的认知能力依赖于其全局动态本体， 由动态知识图表示。 在本研究计划中，我们提出一个多代理系统，以有效地监测和更新大型动态 Azzimov开发的语义电子商务搜索引擎“daneel”的知识图 Inc.拟议的系统将被用作算法工具来分类Azzimov的电子商务服务器 在互联网上的兴趣，并优化高效搜索的图形。构建这样一个工具的挑战之一是 是以接近真实的时间的方式计算大规模动态图的色数。而 计算图的色数是一个组合优化问题， 然而，现有的方法不能满足Azzimov的计算色的要求， 在分布式和动态环境中具有接近真实的时间响应性的大量非常大的图， 比如互联网。 利用所提出的用于计算色数的多代理系统，Azzimov的认知服务系统 能够以更快的速度和准确性监控和更新其动态知识图， 搜索结果基于自适应系统（AAS）体系结构设计了多智能体系统。 Agent的决策模块和交互协议的设计基于一个 分布式图着色算法，这也将在本项目中开发。