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neurips2026-ed-dataset/KGQA

Compressed Multi-Hop KGQA Benchmark Dataset Summary This dataset is a multiple-choice benchmark for evaluating compressed multi-hop reasoning over knowledge graphs. Files data/benchmark.jsonl: main benchmark file. data/question_graphs.jsonl: candidate graph/path evidence for each question. Data Fields Each benchmark example contains: question_id: unique question identifier type: question type question: natural language question… See the full description on the dataset page: https://huggingface.co/datasets/neurips2026-ed-dataset/KGQA.

sourceHugging Faceupdated 5mo agoView on Hugging Face

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Compressed Multi-Hop KGQA Benchmark

Dataset Summary

This dataset is a multiple-choice benchmark for evaluating compressed multi-hop reasoning over knowledge graphs.

Files

—data/benchmark.jsonl: main benchmark file.
—data/question_graphs.jsonl: candidate graph/path evidence for each question.

Data Fields

Each benchmark example contains:

—question_id: unique question identifier
—type: question type
—question: natural language question
—given_entity: starting entity or pair of given entities
—given_entity_qids: Wikidata QIDs of the given entities
—path: gold multi-hop Wikidata path
—a, b, c, d, e: answer options
—answer: gold option label

Intended Use

This benchmark is intended for evaluating whether models can recover latent multi-hop reasoning paths from relation-compressed questions.