RDF data can be analyzed with various query languages such as SPARQL
or SeRQL. Due to their nature these query languages do not support fuzzy queries.
In this paper we present a new method that transforms the information presented
by subject-relation-object relations within RDF data into Activation Patterns. These
patterns represent a common model that is the basis for a number of sophisticated
analysis methods such as semantic relation analysis, semantic search queries, unsuper-
vised clustering, supervised learning or anomaly detection. In this paper, we explain
the Activation Patterns concept and apply it to an RDF representation of the well
known CIA World Factbook.
This project adds a graphical user interface(GUI) for exporting data of Google Refine projects in RDF format. The export is based on mapping the data to a template graph using the GUI.
is a European project funded by the EU as part of the Seventh Research Framework Programme. The full project title is "Meeting the challenges of the farm of tomorrow by integrating Farm Management Information Systems to support real-time management decisions and compliance to standards", and the funding is under the Cooperation programme of the FP7 in the Food, Agriculture, Fisheries and Biotechnologies (Knowledge Based Bio-Economy) theme.
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HEigen is a spectral analysis tool which computes top k eigenvalues and corresponding eigenvectors of extremely large(~billions of nodes and edges) graphs. HEigen runs on top of Hadoop platform.
S. Brants, и S. Hansen. In Proceedings of the Third Conference on Language Resources and Evaluation LREC-02. Las Palmas de Gran Canaria, стр. 1643--1649. (2002)