Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH.

Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu, Xiaoyan Zhu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Satisfaction prediction is one of the prime concerns in search performance evaluation. It is a non-trivial task for two major reasons: (1) The definition of satisfaction is rather subjective and different users may have different opinions in satisfaction judgement. (2) Most existing studies on satisfaction prediction mainly rely on users' click-through or query reformulation behaviors but there are many sessions without such kind of interactions. To shed light on these research questions, we construct an experimental search engine that could collect users' satisfaction feedback as well as mouse click-through/movement data. Different from existing studies, we compare for the first time search users' and external assessors' opinions on satisfaction. We find that search users pay more attention to the utility of results while external assessors emphasize on the efforts spent in search sessions. Inspired by recent studies in predicting result relevance based on mouse movement patterns (namely motifs), we propose to estimate the utilities of search results and the efforts in search sessions with motifs extracted from mouse movement data on search result pages (SERPs). Besides the existing frequency-based motif selection method, two novel selection strategies (distance-based and distribution-based) are also adopted to extract high quality motifs for satisfaction prediction. Experimental results on over 1,000 user sessions show that the proposed strategies outperform existing methods and also have promising generalization capability for different users and queries.