A Distributed Graph Approach for Pre-processing Linked RDF Data Using Supercomputers

Efficient RDF, graph based queries are becoming more pertinent based on the increased interest in data analytics and its intersection with large, unstructured but connected data. Many commercial systems have adopted distributed RDF graph systems in order to handle increasing dataset sizes and complex queries. This paper introduces a distribute graph approach to pre-processing linked data. Instead of traversing the memory graph, our system indexes pre-processed join elements that are organized in a graph structure. We analyze the Dbpedia data-set (derived from the Wikipedia corpus) and compare our access method to the graph traversal access approach which we also devise. Results show from our experiments that the distributed, pre-processed graph approach to accessing linked data is faster than the traversal approach over a specific range of linked queries

Rule-based Discovery in Spatial Data Infrastructures

Answering questions based on spatial data is becoming increasingly important in a variety of domains. Often the required data are distributed and heterogeneous, and several data sources need to be combined in order to derive the information required by a user. Spatial data infrastructures (SDIs) are aimed at making the discovery and access to distributed geographic data more efficient. However, the catalogue services currently used in SDIs for discovering geographic data do not allow expressive queries and do not take into account that more than one data source might be required to answer a question. In this paper, we illustrate how Semantic Web technology can be used to enhance the discovery of geographic data and to generate an answer to a specific question. The approach is based on rules describing mappings between local data and a common domain vocabulary as well as back-ground domain knowledge. We illustrate how these rules can be used to infer information for answering a user’s question and to discover data sources that can be used for these inferences. The approach is illustrated by an example from the domain of disaster management.JRC.H.6-Spatial data infrastructure

Five Stars of Linked Data Vocabulary Use

In 2010 Tim Berners-Lee introduced a 5 star rating to his Linked Data design issues page to encourage data publishers along the road to good Linked Data. What makes the star rating so effective is its simplicity, clarity, and a pinch of psychology -- is your data 5 star? While there is an abundance of 5 star Linked Data available today, finding, querying, and integrating/interlinking these data is, to say the least, difficult. While the literature has largely focused on describing datasets, e.g., by adding provenance information, or interlinking them, e.g., by co-reference resolution tools, we would like to take Berners-Lee\u27s original proposal to the next level by introducing a 5 star rating for Linked Data vocabulary use

An Ontology Design Pattern for Surface Water Features

GIScience 201