Path Ranking with Attention to Type Hierarchies

The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

Transferring Embodied Concepts Between Perceptually Heterogeneous Robots

This paper explores methods and representations that allow two perceptually heterogeneous robots, each of which represents concepts via grounded properties, to transfer knowledge despite their differences. This is an important issue, as it will be increasingly important for robots to communicate and effectively share knowledge to speed up learning as they become more ubiquitous.We use Gӓrdenfors’ conceptual spaces to represent objects as a fuzzy combination of properties such as color and texture, where properties themselves are represented as Gaussian Mixture Models in a metric space. We then use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. These mappings are then used to transfer a concept from one robot to another, where the receiving robot was not previously trained on instances of the objects. We show in a 3D simulation environment that these models can be successfully learned and concepts can be transferred between a ground robot and an aerial quadrotor robot