Knowledge graphs, on top of entities and their relationships, contain another important element: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph modeling focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing knowledge graph models. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models using LiteralE and evaluate the performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based models, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.
KBLRN @cite_4 handles literals in a separate function added to the vanilla scoring function and thus does not incorporate literals in to the entity embeddings themselves. Note, that incorporating the literal information into the embeddings also seems advantageous for entity disambiguation or clustering. The construction of features from the numerical literals in KBLRN is based on some prior knowledge about where the difference between head's and tail's numerical literals is a good predictor for a given relation. Furthermore, KBLRN uses a fixed radial basis function (RBF) to transform these features. In contrast, LiteralE does not use any prior knowledge about the meaning of numerical literals and uses an adaptive function @math as in Equations , , , and to find the joint features of an entity and its literals.
@cite_4
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We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.