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CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

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arxiv 2009.07810 v2 pith:7DFN2VNE submitted 2020-09-16 cs.CL cs.AIcs.IRcs.LG

Tara Safavi , Danai Koutra This is my paper · ORCID
classification cs.CLcs.AIcs.IRcs.LG
keywords codexknowledgecompletiongraphbenchmarkdatasetlinkmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available at https://bit.ly/2EPbrJs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning

    cs.CL 2025-09 reject novelty 6.0 of 10

    SAT uses hierarchical contrastive alignment and a unified graph instruction to tune a lightweight adapter for knowledge graph completion, reporting large link prediction gains.

  2. GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

    cs.CL 2025-11 reject novelty 5.0 of 10

    An LLM can memorize a knowledge graph into LoRA weights and answer relation/reasoning queries about it without graph context, but the evaluation partly trains on the test task.

  3. A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models

    cs.CL 2025-05 conditional novelty 5.0 of 10

    LLM knowledge, measured by self-reported true/false checks on knowledge-graph triplets, shows homophily and degree correlations that a graph neural network exploits to select more effective fine-tuning data.

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