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  3. Grammar transformations of topographic feature type annotations of the U.S. to structured graph data

Grammar transformations of topographic feature type annotations of the U.S. to structured graph data

Dates

Publication Date
Start Date
1994
End Date
1999

Citation

Abbott, E.T., 2024, Grammar transformations of topographic feature type annotations of the U.S. to structured graph data: U.S. Geological Survey data release, https://doi.org/10.5066/P1BDPXKZ.

Summary

These data were used to examine grammatical structures and patterns within a set of geospatial glossary definitions. Objectives of our study were to analyze the semantic structure of input definitions, use this information to build triple structures of RDF graph data, upload our lexicon to a knowledge graph software, and perform SPARQL queries on the data. Upon completion of this study, SPARQL queries were proven to effectively convey graph triples which displayed semantic significance. These data represent and characterize the lexicon of our input text which are used to form graph triples. These data were collected in 2024 by passing text through multiple Python programs utilizing spaCy (a natural language processing library) and [...]

Contacts

Point of Contact :
Dalia E Varanka, U.S. Geological Survey
Originator :
Emily T Abbott
Metadata Contact :
Dalia E Varanka, U.S. Geological Survey
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
SDC Data Owner :
National Geospatial Technical Operations Center
USGS Mission Area :
Core Science Systems

Attached Files

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Metadata_XML.xml
Original FGDC Metadata
View 		25.73 KB application/fgdc+xml
Chunk_Lexicon.csv 40.24 KB text/csv
Chunk_Output.csv 210.45 KB text/csv
Glossary_Definitions.csv 32.44 KB text/csv
Glossary_Natural_Language.csv 34.53 KB text/csv
Token_Lexicon.csv 27.39 KB text/csv
Token_Output.csv 256.39 KB text/csv

Purpose

The characterization and summation of this lexicon gave insight to the grammatical structures and patterns present within the input text. These data were uploaded to a knowledge graph software (OnTop 5, 2024, https://ontop-vkg.org/ and PostgreSQL 14, https://www.postgresql.org/) which accepted SQL queries and returned triples that were semantically significant. Query results obtained from the knowledge graph demonstrated the graph’s ability to detect implicit connections between geospatial concepts in the lexicon.

Rights

This work is marked with Creative Commons Zero v1.0 Universal (https://creativecommons.org/publicdomain/zero/1.0/).

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P1BDPXKZ

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