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Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu
TL;DR
Knowledge-base representation learning has largely focused on direct relations, despite the inference information in multiple-step paths. PTransE selects reliable paths, composes their relation embeddings, and models them as translations between entities. The model reports consistent, significant improvements over TransE and other baselines across knowledge-base completion and relation extraction from text.
Problem
Existing representation-learning methods mainly model direct relations, leaving multiple-step paths and their inference patterns underused.
Method
PTransE measures path reliability with path-constraint resource allocation and represents selected paths through semantic composition of relation embeddings.
Results
PTransE achieves consistent and significant improvements over TransE and other baselines on knowledge-base completion and relation extraction from text.
Takeaways & Limitations
Modeling reliable, composed relation paths provides a representation-learning approach that improves the evaluated knowledge-base and text relation tasks.
Takeaways & Limitations
For computational efficiency, PTransE restricts paths to at most three steps and retains only paths with reliability scores above 0.01.
Abstract
from arXiv · showhide
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.
1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems,
The authors are affiliated with the Samsung R&D Institute of China in Beijing, China.
- The listed affiliation is Samsung R&D Institute of China.
- The institute is located in Beijing.
- The affiliation identifies China as the country.
1 Introduction
Existing representation-learning methods model direct relations, while multiple-step paths encode additional semantic and inference patterns. PTransE extends TransE by selecting and composing reliable paths as translations between entities, with reported improvements across evaluation tasks.
- Motivation: TransE and its extensions consider direct relations but omit multiple-step paths that can indicate semantic relationships and inference patterns.
- PTransE: PTransE extends TransE by treating relation paths as connections between entities and constructing path-based triples.
- Path representation: PTransE composes relations with an operation such as r1 ◦r2 to form a unified path representation optimized as a translation.
- Challenges: Path-based learning is nontrivial because enumerating all paths is impractical, so the implementation limits paths to at most three steps and reliability scores above 0.01.
- Path selection: A path-constraint resource allocation algorithm measures relation-path reliability before reliable paths are selected for learning.
- Evaluation: PTransE significantly outperforms TransE and other baselines on entity prediction, relation prediction, and relation extraction from text.
2 Our Model
PTransE extends TransE by incorporating multiple-step relation paths as translations between entities alongside direct relations. It selects reliable paths, composes their relation embeddings, and trains with direct and path-based objectives.
- Model overview: PTransE augments direct relation triples with triples formed from entity pairs connected by multiple-step relation paths.For a 2-step path, it learns both the individual relations and a composed path relation.
- Model overview: The model combines direct-triple energy with path-triple energy to model both entity–relation correlations and inference correlations among relations.Valid triples are assigned lower scores than invalid triples during optimization.
- Path reliability: PCRA measures relation-path reliability by recursively allocating one unit of resource from the head entity through the path to the tail.The tail's received resource becomes R(p|h, t), and unreliable paths can therefore be excluded from representation learning.
- Relation path representation: Path embeddings are obtained by recursively composing the embeddings of all relations in a path.The paper evaluates addition, multiplication, and recurrent neural network composition operations.
- Relation path representation: For path triples, the energy compares the composed path embedding with the direct relation embedding rather than recomputing a translation with entity embeddings.This score is low when the path is consistent with the direct relation and high otherwise.
- Optimization and implementation details: For computational efficiency, PTransE restricts paths to at most 3 steps and retains paths whose reliability score exceeds 0.01.The restriction addresses the potentially billions of paths arising in large knowledge bases.
3 Experiments and Analysis
Experiments on Freebase datasets evaluate PTransE for knowledge base completion and relation extraction, showing consistent gains over TransE and other baselines while highlighting the value and limits of relation-path modeling.
- 3.2.1 Entity Prediction: PTransE significantly and consistently outperforms other baselines, including TransE, on entity prediction.The authors attribute this improvement to relation paths supplementing direct relational representations.
- 3.2.1 Entity Prediction: Addition-based path composition outperforms multiplication and recurrent neural networks in both Mean Rank and Hits@10.The authors relate this to compatibility with the learning objectives of TransE and PTransE.
- 3.2.1 Entity Prediction: PTransE using relation paths of at most 2 or 3 steps achieves comparable entity-prediction results, suggesting that very long paths may be unnecessary.The experiments restrict path lengths for computational efficiency, and the results support this restriction.
- 3.2.1 Entity Prediction: PTransE consistently achieves significant improvement over TransE across 1-to-1, 1-to-N, N-to-1, and N-to-N relation mapping types.The comparison is reported on FB15K.
- 3.2.2 Relation Prediction: PTransE reduces relation-prediction errors by 41.8% compared with TransE+Rev+Path.Considering relation paths during testing also reduces errors by 17.3% for TransE relative to TransE+Rev.
- 3.3 Relation Extraction from Text: When combined with the text-based model Sm2r, PTransE significantly outperforms TransE in precision, especially for top-ranked triples.This result supports the usefulness of relation-path encoding for relation extraction from text.
- 3.3 Relation Extraction from Text: The relation-extraction comparison is nontrivial because many non-relation triples contain relation paths, which can hurt TransE when paths are used only at testing.The authors state that PTransE addresses this through encoding relation paths during training.
- 3.4 Case Study of Relation Inference: A Freebase case study uses three paths connecting Forrest Gump and English to increase confidence in predicting LanguageOfFilm.The example illustrates inference from multiple relation paths.
4 Related Work
Prior work models multi-relational data in low-dimensional spaces and has applied relation paths to inference, recommendation, retrieval, and expert finding.
- Relational learning commonly encodes entities and relations in a low-dimensional latent space.
- Relation paths have been widely used in social networks and recommender systems, often as discrete symbols processed by graph-based algorithms.
- The Path Ranking algorithm applies relation paths to inference on large-scale knowledge bases, including expert finding and information retrieval.
5 Conclusion and Future Work
PTransE extends knowledge-base representation learning by encoding relation paths alongside entities and relations, using path reliability and semantic composition. It is evaluated on knowledge base completion and relation extraction, with consistent and significant improvements over TransE and other baselines, while more complicated inference patterns remain future work.
- PTransE encodes relation paths to embed both entities and relations in a low-dimensional space.
- Path-constraint resource allocation measures relation-path reliability, while semantic composition represents paths for optimization.
- PTransE is evaluated on knowledge base completion and relation extraction from text.
- PTransE achieves consistent and significant improvements over TransE and other baselines.
- Future Work: The paper only models inference patterns between direct relations and relation paths between two entities, leaving more complicated relational patterns for future work.