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---
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license: cc-by-4.0
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task_categories:
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- graph-ml
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language:
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- en
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tags:
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- semantic-network
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- knowledge-graph
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- wikidata
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- graph-ml
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- semantic-inference
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- token-compression
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pretty_name: 'zelph Binaries: Semantic Networks from Knowledge Bases (e.g., Wikidata)'
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size_categories:
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- 100M<n<1B
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---
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# zelph Binaries Dataset
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This dataset provides pre-compiled binary files (.bin) for use with [zelph](https://zelph.org), a sophisticated semantic network system.
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These binaries are derived from large knowledge bases like Wikidata, optimized for fast loading and efficient querying.
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## Dataset Description
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zelph binaries enable users to work with semantic networks without the need to import raw dumps (e.g., JSON files), which can take hours.
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Instead, these .bin files load in minutes, though they require substantial RAM.
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The dataset includes both full and pruned variants:
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- **Full binaries**: Contain the complete network, suitable for comprehensive use but demanding high RAM (Wikidata: ~ 210 GB RAM).
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- **Pruned binaries**: Reduced versions with removed domains (e.g., biology, chemistry, astronomy) to lower RAM requirements (~ 16 GB RAM) while preserving core connections.
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For detailed information on each binary, including sizes, creation dates, pruning details, and updates, visit [https://zelph.org/binaries](https://zelph.org/binaries).
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## How to Use
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1. Download the desired .bin file from this dataset.
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2. In zelph interactive mode, load it with:
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```
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.load /path/to/your-file.bin
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```
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3. Run queries, define rules, perform inferences or run complete scripts (see [zelph on GitHub](https://github.com/acrion/zelph?tab=readme-ov-file#performing-inference) for details).
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## LLM-Friendly Outputs
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zelph can generate rule-based inferences in a compressed text format optimized for LLM training or processing.
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This uses a token encoder that maps Wikidata IDs (Q/P) to compact UTF-8 symbols (CJK range), reducing input length while preserving structure.
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This feature is currently focused on Wikidata, but it can be adapted for similar use cases.
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Use it to export inferences from loaded binaries for LLM datasets – see the [command documentation on GitHub](https://github.com/acrion/zelph?tab=readme-ov-file#exporting-deduced-facts-to-file) for details.
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## Citation
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If you use this dataset, cite as:
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```
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@dataset{zelph,
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author = {Stefan Zipproth},
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title = {zelph Binaries Dataset},
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year = {2026},
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url = {https://huggingface.co/datasets/acrion/zelph}
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}
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```
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