Knowledge graph entity typing (KGET) aims to predict the missing entity types in knowledge graphs (KG). The relationship between entities and their corresponding types is often expressed using a single relation, hasType. However, hasType has a limited capability for modeling diverse entity-type relationships in the embedding space. In this paper, we first introduce multiple auxiliary relations to model the complex entity-type relationship. We propose an efficient and robust algorithm to group similar entity types together and assign a unique auxiliary relation to each group. Then, with the auxiliary relations, we propose an Asynchronous representation learning framework for KGET, named AsyncET, where entity and type embeddings are updated alternatively. Consequently, the quality of entity embeddings is gradually improved during training by infusing type information. In addition, entity types with different granularities and semantics can be properly modeled in the embedding space. Experimental results show that AsyncET can substantially improve the performance of embedding-based methods on the KGET task and has a significant advantage over state-of-the-art neural network-based methods in terms of model sizes and inference time.

Geometric transformations including translation, rotation, and scaling are commonly used operations in image processing.Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE).Inspired by the synergy, we propose a new KGE model by leveraging all three operations in this work.Since translation, rotation, and scaling operations are cascaded to form a composite one, the new model is named Com-poundE.By casting CompoundE in the framework of group theory, we show that quite a few distanced-based KGE models are special cases of CompoundE.CompoundE extends the simple distance-based scoring functions to relation-dependent compound operations on head and/or tail entities.To demonstrate the effectiveness of CompoundE, we perform three prevalent KG prediction tasks including link prediction, path query answering, and entity typing, on a range of datasets.CompoundE outperforms extant models consistently, demonstrating its effectiveness and flexibility.

Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs).Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring function.Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger model size and hinders applicability to real-world problems (e.g., large-scale KGs or mobile/edge computing).A lightweight modularized KGC solution, called GreenKGC, is proposed in this work to address this issue.GreenKGC consists of three modules: representation learning, feature pruning, and decision learning, to extract discriminant KG features and make accurate predictions on missing relationships using classifiers and negative sampling.Experimental results demonstrate that, in low dimensions, GreenKGC can outperform SOTA methods in most datasets.In addition, low-dimensional GreenKGC can achieve competitive or even better performance against high-dimensional models with a much smaller model size.We make our code publicly available.1

Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants; indeed 8% of worldwide carbon emissions are attributed to the production of cement, a key ingredient in concrete. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements including compressive strength. Specifically for computing, concrete is a major ingredient in the construction of data centers.