Knowledge Graph Embedding via Dynamic Mapping Matrix

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 687–696, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Knowledge Graph Embedding via Dynamic Mapping Matrix Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu and Jun Zhao National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China {guoliang.ji,shizhu.he,lhxu,kliu,jzhao}@nlpr.ia.ac.cn Abstract Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR re- gard a relation as translation from head en- tity to tail entity and the CTransR achieves state-of-the-art performance. In this pa- per, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In TransD, we use two vectors to represent a named sym- bol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to con- struct mapping matrix dynamically. Com- pared with TransR/CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less param- eters and has no matrix-vector multipli- cation operations, which makes it can be applied on large scale graphs. In Experi- ments, we evaluate our model on two typ- ical tasks including triplets classification and link prediction. Evaluation results show that our approach outperforms state- of-the-art methods. 1 Introduction Knowledge Graphs such as WordNet (Miller 1995), Freebase (Bollacker et al. 2008) and Yago (Suchanek et al. 2007) have been playing a piv- otal role in many AI applications, such as relation extraction(RE), question answering(Q&A), etc. They usually contain huge amounts of structured data as the form of triplets (head entity, relation, tail entity)(denoted as (h, r, t)), where relation models the relationship between the two entities. As most knowledge graphs have been built either collaboratively or (partly) automatically, they of- ten suffer from incompleteness. Knowledge graph completion is to predict relations between entities based on existing triplets in a knowledge graph. In the past decade, much work based on symbol and logic has been done for knowledge graph comple- tion, but they are neither tractable nor enough con- vergence for large scale knowledge graphs. Re- cently, a powerful approach for this task is to en- code every element (entities and relations) of a knowledge graph into a low-dimensional embed- ding vector space. These methods do reasoning over knowledge graphs through algebraic opera- tions (see section ”Related Work”). Among these methods, TransE (Bordes et al. 2013) is simple and effective, and also achieves state-of-the-art prediction performance. It learns low-dimensional embeddings for every entity and relation in knowledge graphs. These vector em- beddings are denoted by the same letter in bold- face. The basic idea is that every relation is re- garded as translation in the embedding space. For a golden triplet (h, r, t), the embedding h is close to the embedding t by adding the embedding r, that is h + r ≈ t. TransE …

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