What neural models are used to encode the text?
NBOW, LSTM, attentive LSTM
The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.
Knowledge graphs have been proved to benefit many artificial intelligence applications, such as relation extraction, question answering and so on. A knowledge graph consists of multi-relational data, having entities as nodes and relations as edges. An instance of fact is represented as a triplet (Head Entity, Relation, Tail Entity), where the Relation indicates a relationship between these two entities. In the past decades, great progress has been made in building large scale knowledge graphs, such as WordNet BIBREF0 , Freebase BIBREF1 . However, most of them have been built either collaboratively or semi-automatically and as a result, they often suffer from incompleteness and sparseness. The knowledge graph completion is to predict relations between entities based on existing triplets in a knowledge graph. Recently, a new powerful paradigm has been proposed to encode every element (entity or relation) of a knowledge graph into a low-dimensional vector space BIBREF2 , BIBREF3 . The representations of entities and relations are obtained by minimizing a global loss function involving all entities and relations. Therefore, we can do reasoning over knowledge graphs through algebraic computations. Although existing methods have good capability to learn knowledge graph embeddings, it remains challenging for entities with few or no facts BIBREF4 . To solve the issue of KB sparsity, many methods have been proposed to learn knowledge graph embeddings by utilizing related text information BIBREF5 , BIBREF6 , BIBREF7 . These methods learn joint embedding of entities, relations, and words (or phrases, sentences) into the same vector space. However, there are still three problems to be solved. (1) The combination methods of the structural and textual representations are not well studied in these methods, in which two kinds of representations are merely aligned on word level or separate loss function. (2) The text description may represent an entity from various aspects, and various relations only focus on fractional aspects of the description. A good encoder should select the information from text in accordance with certain contexts of relations. Figure 1 illustrates the fact that not all information provided in its description are useful to predict the linked entities given a specific relation. (3) Intuitively, entities with many facts depend more on well-trained structured representation while those with few or no facts might be largely determined by text descriptions. A good representation should learn the most valuable information by balancing both sides. In this paper, we propose a new deep architecture to learn the knowledge representation by utilizing the existing text descriptions of entities. Specifically, we learn a joint representation of each entity from two information sources: one is structure information, and another is its text description. The joint representation is the combination of the structure and text representations with a gating mechanism. The gate decides how much information from the structure or text representation will carry over to the final joint representation. In addition, we also introduce an attention mechanism to select the most related information from text description under different contexts. Experimental results on link prediction and triplet classification show that our joint models can handle the sparsity problem well and outperform the baseline method on all metrics with a large margin. Our contributions in this paper are summarized as follows.