Knowledge Graph Representation via Similarity-Based Embedding

Scientific Programming, Jul 2018

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

http://downloads.hindawi.com/journals/sp/2018/6325635.pdf

Knowledge Graph Representation via Similarity-Based Embedding

Knowledge Graph Representation via Similarity-Based Embedding Zhen Tan, Xiang Zhao, Yang Fang, Bin Ge, and Weidong Xiao Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, China Correspondence should be addressed to Zhen Tan; nc.ude.tdun@a80nehznat and Xiang Zhao; nc.ude.tdun@oahzgnaix Received 16 March 2018; Revised 27 April 2018; Accepted 13 May 2018; Published 15 July 2018 Academic Editor: Juan A. Gomez-Pulido Copyright © 2018 Zhen Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities. 1. Introduction Knowledge graph (KG), as an important part of the artificial intelligence, is playing an increasingly more essential role in different domains [1]: question answer system [2, 3], information retrieval [4], semantic parsing [5], named entity disambiguation [6], biological data mining [7, 8], and so on [9, 10]. In knowledge graphs, facts can be denoted as instances of binary relations (e.g., PresidentOf (DonaldTrump, American)). Nowadays, a great number of knowledge graphs, such as WordNet [11], Freebase [12], DBpedia [13], YAGO [14], and NELL [15] usually do not appear simultaneously. Instead, they were constructed to describe the structured information in various domains [16], and all of them are fairly sparse. Knowledge representation learning [17–19] is considered as an important task to extract the latent features from associated space. Recently, knowledge embedding [20, 21], an effective method of feature extraction [22], was proposed to compress a high-dimensional and sparse space into a low-dimensional and continuous space. Knowledge embedding can be used to derive new unknown facts from known knowledge bases (e.g., link prediction) and to determine whether a triplet is correct or not (e.g., triplets classification) [23]. Moreover embedding representation [24] has been used to support question answer systems [25] and machine reading [26]. However, almost all embedding models only use the features and attributes in knowledge graph to represent entities and relations, which omits the fact that entities and relations are projections of the facts in independent space. Besides, almost all of them have high time and memory-space complexities and cannot be used in large-scale knowledge graphs. In this research, we propose a novel similarity-based knowledge embedding model, namely, SimE-ER, which calculates the entity and relation similarities between two spaces (independent and associated spaces). A sketch of the model framework is provided in Figure 1. The basic idea of this paper is that independent and associated spaces are used to represent the irrelevant and interconnected entities (relations) features, respectively. In independent space, the features of entities (relations) are independent and irrelevant. By contrast, the features of entities (relations) in associated space are interconnected and interacting, and the entities and relations can be denoted by the entities and relations connected with them. Plus, the similarities of the same entities (relations) with different spaces are high. In Figure 1, we can see that, in independent space, the features of are only constructed by themselves, but, in (...truncated)


This is a preview of a remote PDF: http://downloads.hindawi.com/journals/sp/2018/6325635.pdf

Zhen Tan, Xiang Zhao, Yang Fang, Bin Ge, Weidong Xiao. Knowledge Graph Representation via Similarity-Based Embedding, Scientific Programming, 2018, 2018, DOI: 10.1155/2018/6325635