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.
In the last years, several efforts to incorporate literals into KG embedding methods have been made. Toutantova and Tu make use of textual literals of entities in addition to relational embeddings. More specifically they learn additional entity embeddings from their textual description and use them in an additive term in the score function of latent distant methods. Xie , use image literals in their model by projecting entities' image features into an entity embeddings space. However, all of those approaches do not consider numerical literals. MultiModal @cite_6 extends DistMult to also predict the likeliness of (subject, predicate, literal)-triples, by replacing the object embedding in standard DistMult by its literal embedding (where literals of different modalities are taken into account). By doing so literals are incorporated into entity embeddings by a multi-task learning scheme. In contrast, LiteralE combines the literals into the entity embedding directly and thus no additional learning task is required.
@cite_6
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Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches however primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in relational databases, such as text, images, and numerical values. In our approach, we propose a multimodal embedding using different neural encoders for this variety of data, and combine with existing models to learn embeddings of the entities. We extend existing datasets to create two novel benchmarks, YAGO-10-plus and MovieLens-100k-plus, that contain additional relations such as textual descriptions and images of the original entities. We demonstrate that our model utilizes the additional information effectively to provide further gains in accuracy. Moreover, we test our learned multimodal embeddings by using them to predict missing multimodal attributes.