PUBLICATION
KESeDa: Knowledge Extraction from Heterogeneous Semi-Structured Data Sources
Type
Conference Paper
Year
2016
Authors
Research Area
Intelligent Information Management
Event
13th International Conference on Semantic Systems
Published in
Proceedings of the 13th International Conference on Semantic Systems
ISBN/ISSN
978-1-4503-4752-5
Download
Abstract
A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification. Due to the enormous amount of information the necessary preparation of this data for integrating it in the Web of Data requires automated processes. The extraction of knowledge from structured as well as unstructured data has already been the topic of research. But especially for the semi-structured data format JSON, which is widely used as a data exchange format e.g., in social networks, extraction solutions are missing. Based on the findings we made by analyzing existing extraction methods, we present our KESeDa approach for extracting knowledge from heterogeneous, semi-structured data sources. We show how knowledge can be extracted by describing different analysis and processing steps. With the resulting semantically enriched data the potential of Linked Data can be utilized.
Reference
Seidel, Martin; Krug, Michael; Burian, Frank; Gaedke, Martin: KESeDa: Knowledge Extraction from Heterogeneous Semi-Structured Data Sources. Proceedings of the 13th International Conference on Semantic Systems, pp. 129-136, 2016.
