Automatic Creation of an Ontological Knowledge Base from Grid and Cloud-based Wikipages

Chaitali Gupta, M. Govindaraju
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Abstract

As the size of experimental data, documents, and web pages grows larger, it will be difficult for scientists to search for appropriate data and grid web-services through keyword-based grid search portal interfaces. A well designed and dynamically updated knowledge base can help understand user queries when they are structurally different, but semantically correlated, with actual data stored in a grid or cloud. In this paper we discuss the implementation of a knowledge base creation tool for grid and cloud based wikipages. In order to achieve accurate results in the query match-making process, we store the knowledge base in the rich OWL format and update it automatically with subsequent inference of new facts. Our framework extracts grid and cloud related wikipages and web pages using syntactic Link Grammar Parser and creates core ontology models specific to the grid and cloud domain. In this paper we describe two core components of the ontology generation framework - an information extraction framework and a core ontology model. We present the accuracy of search in terms of precision and recall and its relationship with the dynamically updated ontological domain knowledge base.
从网格和基于云的维基页面自动创建本体知识库
随着实验数据、文档和网页的规模越来越大,科学家很难通过基于关键字的网格搜索门户界面来搜索合适的数据和网格网络服务。当用户查询与存储在网格或云中的实际数据在结构上不同但在语义上相关时,设计良好且动态更新的知识库可以帮助理解用户查询。在本文中,我们讨论了一个基于网格和云的知识库创建工具的实现。为了在查询匹配过程中获得准确的结果,我们将知识库存储在丰富的OWL格式中,并通过后续的新事实推理自动更新知识库。我们的框架使用语法链接语法分析器提取网格和云相关的wiki和网页,并创建特定于网格和云领域的核心本体模型。本文描述了本体生成框架的两个核心组件——信息提取框架和核心本体模型。我们从查准率和查全率两个方面提出了搜索的准确性,以及它与动态更新的本体领域知识库之间的关系。
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