Aligning knowledge graphs from different sources or languages, which aims to align both the entity and relation, is critical to a variety of applications such as knowledge graph construction and question answering. Existing methods of knowledge graph alignment usually rely on a large number of aligned knowledge triplets to train effective models. However, these aligned triplets may not be available or are expensive to obtain for many domains. Therefore, in this paper we study how to design fully-unsupervised methods or weakly-supervised methods, i.e., to align knowledge graphs without or with only a few aligned triplets. We propose an unsupervised framework based on adversarial training, which is able to map the entities and relations in a source knowledge graph to those in a target knowledge graph. This framework can be further seamlessly integrated with existing supervised methods, where only a limited number of aligned triplets are utilized as guidance. Experiments on real-world datasets prove the effectiveness of our proposed approach in both the weakly-supervised and unsupervised settings. Knowledge graphs represent a collection of knowledge facts and are quite popular in the real world. Each fact is represented as a triplet (h, r, t), meaning that the head entity h has the relation r with the tail entity t. Examples of real-world knowledge graphs include instances which contain knowledge facts from general domain in different languages (Freebase 1 , DBPedia BID2 , Yago BID19 , WordNet 2 ) or facts from specific domains such as biomedical ontology (UMLS 3 ). Knowledge graphs are critical to a variety of applications such as question answering BID4 ) and semantic search BID13 ), which are attracting growing interest recently in both academia and industry communities.In practice, each knowledge graph is usually constructed from a single source or language, the coverage of which is limited. To enlarge the coverage and construct more unified knowledge graphs, a natural idea is to integrate multiple knowledge graphs from different sources or languages BID0 ). However, different knowledge graphs use distinct symbol systems to represent entities and relations, which are not compatible. As a result, it is necessary to align entities and relations across different knowledge graphs (a.k.a., knowledge graph alignment) before integrating them.Indeed, there are some recent studies focusing on aligning entities and relations from a source knowledge graph to a target knowledge graph ( BID23 ; BID6 ; BID7 ). These methods typically represent entities and relations in a low-dimensional space, and meanwhile learn a mapping function to align entities and relations from the source knowledge graph to the target one. However, these methods usually rely on a large number of aligned triplets as labeled data to train effective alignment models. In reality, the aligned triplets may not be available or can be expensive to obtain, making existing methods fail to achieve satisfactory results. Therefore, we are seeking for an unsupervised or weakly-supervised approach, which is able to align knowledge graphs with a few or even without labeled data.In this paper, we propose an unsupervised approach for knowledge graph alignment with the adversarial training framework BID11 . Our proposed approach aims to learn alignment functions, i.e., P e (e tgt |e src ) and P r (r tgt |r src ), to map the entities and relations (e src and r src ) from the source knowledge graph to those (e tgt and r tgt ) in the target graph, without any labeled data. Towards this goal, we notice that we can align each triplet in the source knowledge graph with one in the target knowledge graph by aligning the head/tail entities and relation respectively. Ideally, the optimal alignment functions would align all the source triplets to some valid triplets (i.e., triplets expressing true facts). Therefore, we can enhance the alignment functions by improving the plausibility of the aligned triplets. With this intuition, we train a triplet discriminator to distinguish between the real triplets in the target knowledge graph and those aligned from the source graph, which provides a reward function to measure the plausibility of a triplet. Meanwhile, the alignment functions are optimized to maximize the reward. The above process naturally forms an adversarial training procedure BID11 ). By alternatively optimizing the alignment functions and the discriminator, the discriminator can consistently enhance the alignment functions.However, the above approach may suffer from the problem of mode collapse BID17 ). Specifically, many entities in the source knowledge graph may be aligned to only a few entities in the target knowledge graph. This problem can be addressed if the aggregated posterior entity distribution e src P e (e tgt |e src )P (e src ) derived by the alignment functions matches the prior entity distribution P (e tgt ) in the target knowledge graph. Therefore, we match them with another adversarial training framework, which shares similar idea with adversarial auto-encoders BID16 ).The whole framework can also be seamlessly integrated with existing supervised methods, in which we can use a few aligned entities or relations as guidance, yielding a weakly-supervised approach. Our approach can be effectively optimized with stochastic gradient descent, where the gradient for the alignment functions is calculated by the REINFORCE algorithm (Williams (1992)). We conduct extensive experiments on several real-world knowledge graphs. Experimental results prove the effectiveness of our proposed approach in both the weakly-supervised and unsupervised settings.
This paper studies weakly-supervised knowledge graph alignment with adversarial training frameworks.
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