Knowledge Representation Learning with Entities Attributes and Relations
Knowledge Representation Learning with Entities, Attributes and Relations Yankai Lin, Zhiyuan Liu, Maosong Sun Tsinghua University May 22, 2016
Knowledge Graph Completion • Goal – Infer the missing fact in Knowledge Graph 2
Knowledge Graph Completion • Based on Graph Features • Based on Representation Learning – Tensor Factorization – Implicit Space – Geometric space 3
Based on Graph Features • Path Ranking Algorithm – Path Features – Classifier • Example – school 4
Tensor Factorization • RESCAL 5
Implicit Space • • Structure Embedding (SE) Semantic Matching Energy (SME) Neural Tensor Network (NTN) … 6
Geometric space • Trans. E – Embedding: • Entity Vectors • Relation Vectors – Goal: • Extension – Trans. H, Tran. R, Trans. D … 7
Can we do better? • Fact in Knowledge Graph – Entity Attributes – Relations between Entities • Embed with different Models! 8
Existing Problem • Attributes vs. Relations 9
Our Model • Relational Triple Encoder • Attributional Triple Encoder 10
Relational Triple Encoder • Traditional Knowledge Representation Model – Trans. E – Trans. R –… 11
Attributional Triple Encoder • Classifier – Neural Network –… • Attribute Correlations – Profession • Gender • Education –… 12
Empirical Evaluation • Data – FB 24 k • Task – Entity Prediction – Relation Prediction – Attributes Prediction 13
Entity Prediction • (head, relation, ? ) • (? , relation, tail) 14
Relation Prediction • Correlation between Relations & Attributes (CRA) – General Form of Type Constraint 15
Attribute Prediction • Attribute Correlations (AC) 16
Example 17
Conclusion • Distinguish existing KG-relations into attributes and relations • Propose a new KR model (KR-EAR) • Encode the correlations between entity attributes in KR-EAR. 18
Future Work • Combine Probabilistic Graphic Model with Knowledge Representation • Split relations and attributes by held-out methods 19
- Slides: 19