MEL: Metadata Extractor & Loader

Sergio J. Rodríguez Méndez; Pouya G. Omran; Armin Haller; Kerry Taylor

MEL: Metadata Extractor & Loader

Sergio J. Rodríguez Méndez, Pouya G. Omran, Armin Haller, Kerry Taylor

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

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.

Original language	English
Number of pages	5
Publication status	Published - 2021
Event	2021 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2021 - Virtual, Online Duration: 24 Oct 2021 → 28 Oct 2021

Conference

Conference	2021 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2021
City	Virtual, Online
Period	24/10/21 → 28/10/21

Cite this

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title = "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.",

keywords = "Data Analysis Pipeline, Data Pre processing, Information Extraction, Knowledge Graph Construction, Metadata Extraction",

author = "{Rodr{\'i}guez M{\'e}ndez}, {Sergio J.} and Omran, {Pouya G.} and Armin Haller and Kerry Taylor",

note = "Publisher Copyright: {\textcopyright} 2021 CEUR-WS. All rights reserved.; 2021 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2021 ; Conference date: 24-10-2021 Through 28-10-2021",

year = "2021",

language = "English",

}

TY - CONF

T1 - MEL

T2 - 2021 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters-Demos-Industry 2021

AU - Rodríguez Méndez, Sergio J.

AU - Omran, Pouya G.

AU - Haller, Armin

AU - Taylor, Kerry

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Data Analysis Pipeline

KW - Data Pre processing

KW - Information Extraction

KW - Knowledge Graph Construction

KW - Metadata Extraction

UR - http://www.scopus.com/inward/record.url?scp=85117698070&partnerID=8YFLogxK

UR - https://ceur-ws.org/Vol-2980/

M3 - Paper

AN - SCOPUS:85117698070

Y2 - 24 October 2021 through 28 October 2021

ER -

MEL: Metadata Extractor & Loader

Abstract

Conference

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