# An Approach to Knowledge Base Completion
## 1 Introduction  
Large-scale knowledge bases such as Freebase [1], DBPedia [2], and NELL [3] are now publicly available, and they contain massive volumes of facts involving diverse entities and relations. Due to their huge size, they are used as an essential resource in many language-related tasks such as information retrieval, question-answering, and text mining. However, no matter how large the knowledge bases are, they are not yet complete since they are constructed manually. For instance, Freebase has three million entities for the 'Person' concept, but only 25% of them have nationality information [4]. In addition, the entities with parent information occupy just 6% of the 'Person' entities, while it is natural that every person has a nationality or parent(s) in the real world. Such missing information appears for nearly every relation, and then is piled up. As a result, these cumulative missing facts limit the effective use of knowledge bases. Therefore, it is significant to fill in missing facts of a knowledge base automatically. There have been many previous studies that attempted to fill in missing facts. One promising approach to this task is to use knowledge graph embedding [5; 6; 7; 8; 9], which represents all entities and relations of a knowledge base as vectors in a low-dimensional space. The candidates for missing facts are also represented as vectors in the low-dimensional space and their plausibility is measured by vector similarity to the existing facts in the space. Then, the candidates with high plausibility are discovered as possible facts for the knowledge base. According to the experimental results of previous studies [10], this approach achieves over 70% in Hits@10 (Hits@k is the rate of correct entities appearing at top-k ranked entities. That is, Hits@10 is the proportion of correct entities ranked in the top 10) in knowledge base completion. However, its Hits@1 is lower than 30%. This implies that it is still difficult to find correct missing facts using knowledge graph embedding alone. A couple of methods have been proposed to improve Hits@1 performance in discovering missing facts. [PERSON] et al. conducted knowledge base completion using both knowledge graph embedding and rules derived from knowledge base schema [11]. Their experimental results show that adopting the rules helps improve Hits@1 performance. However, their method still does not cover \(n\)-to-\(n\) relations, since it is based on knowledge graph embedding which utilizes the characteristics of 1-to-1 relations. On the other hand, [PERSON] et al. proposed a re-ranking model that uses both internal and external information of a knowledge graph for more accurate knowledge base completion [12]. Their model first extracts top-\(k\) candidates according to the plausibility computed by knowledge graph embedding. Then, the candidates are re-ranked by considering two kinds of additional information: knowledge base schema information and web search results. This is a good approach in that it is the first attempt to exploit such additional information, but it has still some problems. The main problem of the model is that it depends severely on the first top-\(k\) results. If a correct fact is excluded in the first step, there is no chance to include it in the second step. Note that, in this model, the top-\(k\) candidates are determined by a single knowledge graph embedding while no knowledge graph embedding is yet perfect. As a result, many correct facts are missed by this model. This paper proposes a sole ranking model that adopts a committee of knowledge graph embeddings for accurate knowledge base completion. Unlike previous work that represents knowledge base completion as a re-ranking task [12], we formulate it as a ranking task. Given a knowledge base, the proposed model generates candidate facts, and then the plausibility of each candidate is determined by a committee of knowledge graph embeddings, not by a single knowledge graph embedding. After that, the candidates are sorted according to their plausibility.