Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph (KG).Multilingual KGs associate entities and relations with surface forms written in different languages.An entity or relation may be associated with distinct IDs in different KGs, necessitating entity alignment (EA) and relation alignment (RA).Many effective algorithms have been proposed for completion and alignment as separate tasks.Here we show that these tasks are synergistic and best solved together.Our multitask approach starts with a state-of-the-art KG embedding scheme, but adds a novel relation representation based on sets of embeddings of (subject, object) entity pairs.This representation leads to a new relation alignment loss term based on a maximal bipartite matching between two sets of embedding vectors.This loss is combined with traditional KGC loss and optionally, losses based on text embeddings of entity (and relation) names.In experiments over KGs in seven languages, we find that our system achieves large improvements in KGC compared to a strong completion model that combines known facts in all languages.It also outperforms strong EA and RA baselines, underscoring the value of joint alignment and completion.

Research on temporal knowledge bases, which associate a relational fact (s, r, o) with a validity time period (or time instant), is in its early days.Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space.TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding stateof-the-art results on both prediction tasks.We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms.In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

A corpus of inference rules between a pair of relation phrases is typically generated using the statistical overlap of argument-pairs associated with the relations (e.g., PATTY, CLEAN).We investigate knowledge-guided linguistic rewrites as a secondary source of evidence and find that they can vastly improve the quality of inference rule corpora, obtaining 27 to 33 point precision improvement while retaining substantial recall.The facts inferred using cleaned inference rules are 29-32 points more accurate.