CoDEx: A Compre… (2020-09-16T00:00:00.000000Z)

TL;DR

CoDEx is distinguished 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.

Abstract

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 this https URL.

Authors

Tara Safavi

1 papers

Danai Koutra

2 papers

References75 items

Fast and Exact Rule Mining with AMIE 3

Knowledge Base Completion: Baseline strikes back (Again)

You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

TL;DR

Abstract

Authors

Tara Safavi

1 papers

Danai Koutra

2 papers

TL;DR

Abstract

Authors

References75 items

Fast and Exact Rule Mining with AMIE 3

Knowledge Base Completion: Baseline strikes back (Again)

You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

TL;DR

Abstract

Authors

References75 items

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