项目名称: 海量RDF数据探索式搜索关键技术与系统研究
项目编号: No.61472426
项目类型: 面上项目
立项/批准年度: 2015
项目学科: 自动化技术、计算机技术
项目作者: 陈跃国
作者单位: 中国人民大学
项目金额: 80万元
中文摘要: 随着越来越多的语义网知识库使用资源描述框架RDF来表达信息实体及它们之间的联系,出现了一大批海量的RDF数据集。利用RDF数据管理系统,人们可以查询、搜索、分析和发现海量RDF数据中蕴含着的丰富语义信息。然而,当前RDF数据查询检索普遍采用一次性的提交查询-返回结果的交互模式,因查询语言表达能力不足或易用性差等问题,满足不了用户在没有明确的查询目标前提下交互性的探索和浏览RDF数据库的需求,无法支持用户随着交互过程而发现和学习海量RDF数据中有趣的知识内容和结构。为此,我们借鉴探索式搜索的概念,提出在存储海量RDF数据的数据库上支撑探索式搜索的交互模型和关键技术。我们的研究将围绕探索式搜索的基本原语、查询语言、交互界面、查询优化和处理等关键技术展开。此外,我们将在集群环境下,研究高性能的海量RDF数据关联分析处理技术,实现支撑海量RDF数据的探索式搜索的数据库原型系统。
中文关键词: 数据库;探索式搜索;RDF;图数据库;大数据分析
英文摘要: Nowadays, with more and more Web of data represented by RDF(resource description framework), a number of massive RDF datasets are available for public, describing entities and their relationships. With RDF data management systems, people are able to query, search, analyze, and discovery rich semantics from massive RDF data. However, most existing techniques on RDF data query processing adopt the query-response paradigm. Due to the deficiency of query languages, they cannot meet the requirements of exploring and browsing RDF database when users do not have very clear search intention. They do not support users to find and learn interesting patterns and knowledge as they interact with the database system. As such, inspired by the concept of exploratory search, we propose a study on key technology in exploratory search over massive RDF data. Our study will be focused on the key components of exploratory search: primitives, query language, user interface, query processing and optimization,etc. Moreover, we will study on high performance semantic analysis of massive RDF data. We will implement a prototype of RDF database system that supports exploratory search over massive RDF data.
英文关键词: database;exploratory search;RDF;graph database;big data analysis