Papers with LOD

4 papers
The LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction (L18-1)

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Challenge: Linked Open Data (LOD) principles are used to export natural language processing (NLP) results to graph-based knowledge base.
Approach: They propose a method for exporting NLP results to a graph-based knowledge base using Linked Open Data principles.
Outcome: The proposed method is available as an open source component for the GATE framework and is available on GitHub.
Towards a Linked Open Data Edition of Sumerian Corpora (L18-1)

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Challenge: Linguistic Linked Open Data (LLOD) is a flourishing line of research in the language resource community . existing LLOD standards and vocabularies are not widely used in this community despite its popularity .
Approach: They propose to use Linguistic Linked Open Data to link a Sumerian corpus with lexical resources . they use a linguistically annotated archive to create a corpus of cuneiform texts .
Outcome: The proposed LLOD framework is used in assyriology, with philological resources underrepresented . the proposed framework is based on a linguistically annotated corpus of Sumerian texts .
Tag Me If You Can! Semantic Annotation of Biodiversity Metadata with the QEMP Corpus and the BiodivTagger (2020.lrec-1)

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Challenge: Dataset Retrieval is based on metadata, structured information about the primary data.
Approach: They propose an ontology-based information extraction pipeline for biodiversity metadata that combines ontologies with semantic annotations to facilitate search.
Outcome: The proposed pipeline is the first annotated metadata corpus for biodiversity research data.
From Linguistic Linked Data to Big Data (2024.lrec-main)

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Challenge: Language data on the LOD cloud has grown in number, size, and variety . Linked (Open) Data (LLOD) is a standardized way of representing and sharing linguistic datasets .
Approach: They propose to combine LLOD and Big Data to improve interoperability of linguistic datasets . they propose to use a machine-readable format to represent and share linguistic data .
Outcome: This paper examines the use cases of Linked (Open) Data and Big Data in language data.

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