entity
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primes
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embedding
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hot spring(s)
spring,inns,located in the administrative territorial entity,public bath in japan,facilities,street address,has facility,temperature,fee,japanese hot spring and its associated bathing facilities and inns,phone number,country,bathing,hot springs of japan,spa,open days,mineral spring
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flat
unit,residence,apartment buildings,building part,self-contained housing unit occupying part of a building,part,housing,building,apartment building
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phosphate works
processing,phosphates, or selling phosphates,industry, processing,business of mining,business,mining,chemical industry
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field(s)
purposes,area of land used for agricultural purposes,land,fields,arable land,rural area,area
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icecap dome
a comparatively elevated area on an icecap,icecap,area
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wall
defines,separation barrier, usually solid, that defines and sometimes protects an area,architectural structure,fencing structure,house,structure,walls,vertical structure,area,support,fortification,building
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section of wadi
wadi,water area
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temp work office
type,work,room,facility,offices,people,workplace,office,room where people perform office work
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intermittent lakes
karst area,seasonal lakes of pivka,group of interconnected lakes
"[-0.03582056611776352, 0.010864040814340115, -0.009265778586268425, -0.004841347690671682, -0.03267(...TRUNCATED)
maneuver area
"tract,exercises,human-geographic territorial entity,tract of land where military field exercises ar(...TRUNCATED)
"[-0.01975652389228344, 0.016831019893288612, -0.007792313117533922, 0.01260372344404459, -0.0230557(...TRUNCATED)

GeoNames Semantic Primes

Dataset Overview

We propose a dataset at the core of our semantic towers methodology which combines vectorized knowledge graph information to augment a Retrieval-and-Generation (RAG) pipeline.

Dataset Construction

The dataset is constructed by deriving and building the semantic tower - an ensemble of primitive semantic information related to a term - of 660 category classes related to geographical locations. These locations are themselves classified into 9 higher-level categories, e.g. H for stream, lake, and sea, and R for road and railroad and are derived from the original GeoNames dataset.

The semantic tower encompasses information gathered from Wikidata, specifically:

  • label
  • instance of
  • subclass of
  • part of
  • represents
  • description

This information forms the smallest subset of knowledge needed to distinguish a term from another.

Embeddings Generation

The vector embeddings are generated using the General Text Embeddings (GTE) large model.

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