Knowledge Extraction from RDF Data with Activation Patterns

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Provided by: Journal of Universal Computer Science
Topic: Big Data
Format: PDF
RDF data can be analyzed with various query languages such as SPARQL. However, due to their nature these query languages do not support fuzzy queries that would allow them to extract a broad range of additional information. In this paper the authors 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, unsupervised clustering, supervised learning or anomaly detection.
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