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WordNet 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 4 term types (noun, verb, adverb, adjective). These term typed are derived from a data dump from the original WordNet 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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