MEL: Metadata Extractor & Loader

Abstract

The metadata and content-based information extraction tasks from heterogeneous file sets are pre-processing steps of many Knowledge Graph Construction Pipelines (KGCP). These tasks often take longer than necessary due to the lack of proper tools that integrate several complementary extraction methods and properties to get a rich output set. This paper presents MEL, a Python-based tool that implements a set of methods to extract metadata and content-based information from unstructured information encoded in different source document formats. The results are generated as JSON files, which can: (a) optionally be stored in a document store, and (b) easily be mapped to RDF using a variety of tools such as J2RM. MEL supports more than 20 different file types, making it a versatile tool that aids pre-processing tasks as part of a KGCP based on comprehensive configurable settings.