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name: relation-extraction-pipeline description: "Prepare relation extraction datasets and inspect the TensorFlow PCNN training workflow for Agriculture_KnowledgeGraph." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Relation Extraction Pipeline

Use this sub-skill when the task is to create, validate, split, or troubleshoot the remote-supervised relation-extraction dataset, or to understand the TensorFlow PCNN training stack in this agriculture knowledge-graph repository.

Route here for

• Turning aligned Wikidata/Wikipedia sentence rows into rel2id.json, entity2id.json, dataset.json, word2vec.json, train_dataset.json, and test_dataset.json.
• Validating six-column training TSV rows and the JSON schemas expected by the PCNN data loader.
• Diagnosing Fire command names, working-directory-sensitive paths, stale _processed_data, bad entity positions, relation-label filters, or NA-sample split failures.
• Reviewing the PCNN model configuration, TensorFlow 1.x assumptions, GPU settings, and large word-vector limits before any expensive training run.

Route elsewhere

• General Wikidata crawling, Wikidata JSON-to-CSV conversion, Neo4j import CSV creation, Hudong/weather crawlers, and network collection pipelines belong to the crawler/Wikidata workflow, not this sub-skill.
• Django relation-label annotation UI, Mongo-backed tagging pages, and web-service startup belong to the web-app workflow, not this sub-skill.
• Neo4j graph querying or Cypher import plans belong to graph query/data management.

Operating map

1. For the end-to-end data build sequence, required source artifacts, Fire commands, and safe replacement commands, read dataset preparation.
2. For exact row and JSON schemas, path-sensitive generated files, and validator usage, read data formats.
3. Before training or editing config.py/train.py, read PCNN training.
4. For known failure modes and recovery actions, read troubleshooting.

Bundled scripts

• scripts/relation_dataset_schema_check.py validates tiny training TSV, rel2id, entity2id, dataset JSON, and optional word-vector JSON files without importing TensorFlow.
• scripts/deduplicate_training_rows.sh deduplicates training rows with explicit input and output paths and a safe --help path.

Safe default

Do not launch PCNN training, live Neo4j alignment, Mongo annotation, network crawling, or large word-vector conversion as a first check. First run the schema checker or a tiny deduplication fixture, then escalate only after data files, working directory, and TensorFlow 1.x/GPU constraints are explicit.