CAREER: Architecting A Database Management System for Semantic Web Data

语义Web的目标是将Web数据从控制它们的应用程序中解放出来，这样数据就可以很容易地描述和交换。这是通过用语句形式的机器可读元数据（例如，X -a - person、X -name Joe、X -age 35）补充自然语言和在Web上找到的其他数据来实现的，并启用数据本体的描述，以便通过本体映射集成来自不同应用程序的数据。这一愿景的一个要素是将Web变成一个巨大的数据库，人们可以对其发出结构化查询并接收结构化答案作为响应。SW-Store项目正在进行一个DBMS的全新设计，该DBMS专门为这种类型的Web元数据和流行的语义Web数据模型（资源描述框架，RDF）而构建。语义Web数据的管理提出了许多困难的挑战。数据的规模正在迅速增长，理论上可以达到Web的规模。查询类型的复杂性差别很大，从关键字搜索到复杂的参数化子图匹配。数据集成、推断和推理必须是基本操作，可以在没有人为干预的情况下大规模操作。数据管理系统不仅必须是存储数据和访问数据的地方；它必须使用数据的机器可读语义来开发更高级的模型，并帮助指导用户浏览大量的信息。总而言之，语义网的数据管理系统将与标准的、事务的、关系的数据库系统非常不同。SW-store项目研究了这样一个系统的体系结构。这项研究本质上是跨学科的，它引入了来自数据管理、语义网和人工智能社区的想法。该项目涉及到对分区方案的试验，其中数据被分配到无共享集群上的不同节点，以便查询可以在多台机器上并行运行。它还涉及探索如何将本体推理集成到数据库系统中，以便从无共享集群提供的近乎无限的可伸缩性中获益。SW-Store进一步研究提供迭代查询接口和将复杂查询与文本搜索集成。最后，该项目涉及到研究语义Web数据管理系统的存储层设计，研究应该如何布局数据，应该执行更新，以及应该创建哪些物化视图。有关该项目的进一步信息可在该项目的网页：http://db.cs.yale.edu/swstore/上找到

The goal of the Semantic Web is to free Web data from the applications that control them, so that data can be easily described and exchanged. This is accomplished by supplementing natural language and other data found on the Web with machine readable metadata in statement form (e.g., X is-a person, X has-name Joe, X has-age 35) and enabling descriptions of data ontologies so that data from different applications can be integrated through ontology mapping. One element of this vision is to turn the Web into a giant database, against which one can issue structured queries and receive structured answers in response.The SW-Store project is undertaking the clean-slate design of a DBMS specifically architected for this type of Web metadata and the prevalent Semantic Web data model, the Resource Description Framework, or RDF. The management of Semantic Web data presents many difficult challenges. The size of the data is growing rapidly, and in theory could reach the scale of the Web. The types of queries vary greatly in complexity, ranging from keyword search to complicated parameterized subgraph matching. Data integration, inference, and reasoning must be primitive operations that can operate at scale without human intervention. A data management system must not only be a place where data is stored and from which data is accessed; it must use the machine-readable semantics of the data to develop higher level models and help guide a user through the mass of information. In sum, a data management system for the Semantic Web will look very different from a standard, transactional, relational database system.The SW-store project researches the architecture of such a system. This research is inherently interdisciplinary, bringing in ideas from the data management, Semantic Web, and artificial intelligence communities. The project involves experimenting with partitioning schemes, where data is allocated to different nodes on a shared-nothing cluster so that queries can be run in parallel across multiple machines. It also involves exploring how ontology reasoning can be integrated inside the database system so that it can benefit from the near limitless scalability a shared-nothing cluster can offer. SW-Store further investigates providing iterative query interfaces and integrating complex queries with text search. Finally, the project involves studying the design of the storage layer for a Semantic Web data management system, looking at how data should be laid out, updates should be performed, and what materialized views to create.Further information about the project can be found at the project Webpage: http://db.cs.yale.edu/swstore/