Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.
Advances in bringing both worlds together include the work of and , where a latent factor model is used, as well as a set of logical rules. An error-term over the rules is added to the classical latent factor objective function. In , a fully differentiable neural theorem prover is used to handle both facts and rules, whereas use adversarial training to do so. learned first-order logic embeddings from formulae learned by ILP. Similar proposals for integrating logical knowledge in distributional representations of words include the work of . Conversely, learn a latent factor model over the facts only, and then try to extract rules from the learned embeddings. @cite_12 proposed to use projections of the subject and object entity embeddings that conserve transitivity and symmetry.
@cite_12
2460423734
This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively.