Harmonizing Metadata of Language Resources for Enhanced Querying and Accessibility

This paper addresses the harmonization of meta-data from diverse repositories of language resources (LRs). Leveraging linked data and RDF techniques, we integrate data from multiple sources into a unified model based on DCAT and META-SHARE OWL ontology. Our methodology supports text-based search, faceted browsing, and advanced SPARQL queries through Linghub, a newly developed portal. Real user queries from the Corpora Mailing List (CML) were evaluated to assess Linghub’s capability to satisfy actual user needs. Results indicate that while some limitations persist, many user requests can be successfully addressed. The study highlights significant metadata issues and advocates for adherence to open vocabularies and standards to enhance metadata harmonization. This initial research underscores the importance of API-based access to LRs, promoting machine usability and data subset extraction for specific purposes, paving the way for more efficient and standardized LR utilization.

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Published
2024-12-20
DOI
https://doi.org/10.1109/cait64506.2024.10963006
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Harmonizing Metadata of Language Resources for Enhanced Querying and Accessibility

Zixuan Liang
Natural Language Processing Techniques
article

Harmonizing Metadata of Language Resources for Enhanced Querying and Accessibility

Zixuan Liang
article en

Abstract

This paper addresses the harmonization of meta-data from diverse repositories of language resources (LRs). Leveraging linked data and RDF techniques, we integrate data from multiple sources into a unified model based on DCAT and META-SHARE OWL ontology. Our methodology supports text-based search, faceted browsing, and advanced SPARQL queries through Linghub, a newly developed portal. Real user queries from the Corpora Mailing List (CML) were evaluated to assess Linghub’s capability to satisfy actual user needs. Results indicate that while some limitations persist, many user requests can be successfully addressed. The study highlights significant metadata issues and advocates for adherence to open vocabularies and standards to enhance metadata harmonization. This initial research underscores the importance of API-based access to LRs, promoting machine usability and data subset extraction for specific purposes, paving the way for more efficient and standardized LR utilization.

Harrisburg University of Science and Technology (US)
Quality Education
Openalex Percentile: Top 73%
Natural Language Processing Techniques
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