Papers with triple classification

2 papers
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization (2020.acl-main)

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Challenge: Existing knowledge graph embedding methods do not allow for the prediction of new triples, such as for search personalization tasks.
Approach: They propose a relational memory network to encode potential dependencies in relationship triples by a transformer self-attention mechanism.
Outcome: The proposed model obtains state-of-the-art results on SEARCH17, WN11 and FB13 for the search personalization task, and on a convolutional neural network-based decoder.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark (2020.emnlp-main)

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Challenge: Knowledge graph completion benchmarks for knowledge graphs are often incomplete . however, the field has remained static over the past decade .
Approach: They propose to use Wikidata and Wikipedia to improve on existing benchmarks . they analyze logical relation patterns, then perform baseline link prediction and triple classification .
Outcome: The proposed datasets improve upon existing benchmarks in scope and difficulty.

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