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Modeling Relation Paths for Representation Learning of Knowledge Bases

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arxiv 1506.00379 v2 pith:W3VSSAOZ submitted 2015-06-01 cs.CL

Yankai Lin , Zhiyuan Liu , Huanbo Luan , Maosong Sun , Siwei Rao , Song Liu This is my paper · ORCID
classification cs.CL
keywords relationpathslearningrepresentationentitiesknowledgemodelbases
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Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.

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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. Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    Introduces the relation set completion task and RelSetE embedding model to infer missing entity-compatible relations in knowledge graphs by capturing latent patterns among observed relations.

  2. Distributional Negative Sampling for Knowledge Base Completion

    cs.LG 2019-08 reject novelty 6.0 of 10

    Distributional negative sampling, which selects corrupted entities by cosine similarity to the original entity, improves several knowledge-base completion metrics for TransE and RESCAL on three benchmarks, though not ...

  3. Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding

    cs.AI 2019-09 conditional novelty 5.0 of 10

    With margin-based losses that allow positive triples to score below a bound rather than exactly zero, TransE can encode symmetric and reflexive relations, and a new complex-space variant TransComplEx gives competitive...

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