KESeDa: Knowledge Extraction from Heterogeneous Semi-Structured Data Sources

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.