U.S. Department of Energy

Pacific Northwest National Laboratory

Semantic Database Systems

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An increasing amount of data of various kinds is being represented in the form of Resource Description Framework (RDF) triples. We can easily amass volumes of data, but can we effectively analyze the data, especially when the most useful information occurs as patterns in the data rather than individual items? The semantic database task addresses this daunting problem by creating a framework on the CRAY XMT with which developers and analysts can quickly create ad-hoc queries and analyses, but which abstracts away many of the intricacies of multi-threaded programming.

The CRAY XMT’s large, shared memory and latency tolerance make it an ideal candidate for hosting large, complex knowledge repositories in the form of typed, directed graphs. Our overall goal is to provide a software and algorithms base that will make it easy to use the XMT for semantic database applications.
Our main approach is to build a prototype system that maintains a semantic graph in the shared memory of the XMT with an information conduit to a front-end workstation. The workstation may present different user interfaces such as SPARQL or another graph query language. An API we design will provide each user interface with a way to initiate data searches in the semantic graph and receive the results.

The XMT is known to be good at solving graph problems with sparseness and irregularity. The semantic graphs are a generalization of this in that each node may have properties attached and each edge will have a type and a direction. We are developing efficient graph data structures capable of supporting these types of general graphs and algorithms that search these graphs for user-specified patterns. Performing optimization techniques on the search queries is critical to the viability of the system.
Within the framework of the semantic database system, functions beyond the building of the graph and searching for patterns (querying) are essential to realizing the full potential of the knowledge representation and information retrieval system. Some of these functions include inferencing such as RDFS closure and Owl Horst Semantics. In addition to these foundational features, extensions will be investigated such as expressing (e.g., in SPARQL) and executing path and other subgraph queries.

Website: http://cass-mt.pnl.gov/research/default.aspx

Article Title: Semantic Database

Article Added: 2010/08/20

Category(s): Intelligence Analysis

Last Update: 13 July 2011 | Pacific Northwest National Laboratory