Neural-Wikipedian
A neural network system that generates English Wikipedia-style biographies from Semantic Web triples using encoder-decoder models.
Overview
Neural-Wikipedian is a research project that adapts encoder-decoder neural network frameworks to automatically generate textual summaries (biographies) from structured Semantic Web triples. It addresses the challenge of transforming machine-readable knowledge base data into coherent, human-readable narratives, which is valuable for automating content creation and enhancing data accessibility.
Key Features
Triple-to-Text Generation — Converts sets of RDF triples (from DBpedia and Wikidata) into fluent English biography summaries. Dual Dataset Support — Includes aligned datasets of DBpedia and Wikidata triples paired with Wikipedia biographies for training and evaluation. Neural Architectures — Implements both Triples2LSTM and Triples2GRU models using the Torch framework for sequence generation. Baseline Language Model — Provides a KenLM n-gram language model as a comparative baseline for summary generation. Pre-trained Models — Offers downloadable pre-trained models for immediate inference without requiring full training cycles.
Philosophy
The project approaches biography generation as a structured data-to-text translation problem, leveraging neural networks to learn the linguistic patterns and factual associations present in Wikipedia content.
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