OpenGloss: A Synthetic Encyclopedic Dictionary and Semantic Knowledge Graph
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Abstract (arXiv)
We present OpenGloss, a synthetic encyclopedic dictionary and semantic knowledge graph for English that integrates lexicographic definitions, encyclopedic context, etymological histories, and semantic relationships in a unified resource. OpenGloss contains 537K senses across 150K lexemes, on par with WordNet 3.1 and Open English WordNet, while providing more than four times as many sense definitions. These lexemes include 9.1M semantic edges, 1M usage examples, 3M collocations, and 60M words of encyclopedic content. Generated through a multi-agent procedural generation pipeline with schema-validated LLM outputs and automated quality assurance, the entire resource was produced in under one week for under 1,000. This demonstrates that structured generation can create comprehensive lexical resources at cost and time scales impractical for manual curation, enabling rapid iteration as foundation models improve. The resource addresses gaps in pedagogical applications by providing integrated content -- definitions, examples, collocations, encyclopedias, etymology -- that supports both vocabulary learning and natural language processing tasks. As a synthetically generated resource, OpenGloss reflects both the capabilities and limitations of current foundation models. The dataset is publicly available on Hugging Face under CC-BY 4.0, enabling researchers and educators to build upon and adapt this resource.
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References · 23
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
- 2016Complex Embeddings for Simple Link Predictioncited by 902
- 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
- 2019KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representationcited by 356
- 2019KG-BERT: BERT for Knowledge Graph Completioncited by 324
- 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
- 2017Lexically Constrained Decoding for Sequence Generation Using Grid Beam Searchcited by 237
- 2023Efficient Guided Generation for Large Language Modelscited by 200
- 2020CoLAKE: Contextualized Language and Knowledge Embeddingcited by 86
11 more
- 2023Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Textcited by 48
- 2025AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corporacited by 23
- 2024The Files are in the Computer: On Copyright, Memorization, and Generative AIcited by 21
- 2022BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Modelscited by 20
- 2023AutoKG: Efficient Automated Knowledge Graph Generation for Language Modelscited by 17
- 2024TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Taskscited by 14
- 2024LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexiconscited by 11
- 2024iText2KG: Incremental Knowledge Graphs Construction Using Large Language Modelscited by 11
- 2025SEMMA: A Semantic Aware Knowledge Graph Foundation Modelcited by 8
- 2022Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddingscited by 3
- 2023PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertipscited by 3
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