Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

Self-Organized Polynomial-Time Coordination Graphs

Coordination graph is a promising approach to model agent collaboration in multi-agent reinforcement learning. It conducts a graph-based value factorization and induces explicit coordination among agents to complete complicated tasks. However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values. It refers to the decentralized constraint optimization problem (DCOP), which and whose constant-ratio approximation are NP-hard problems. To bypass this systematic hardness, this paper proposes a novel method, named Self-Organized Polynomial-time Coordination Graphs (SOP-CG), which uses structured graph classes to guarantee the accuracy and the computational efficiency of collaborated action selection. SOP-CG employs dynamic graph topology to ensure sufficient value function expressiveness. The graph selection is unified into an end-to-end learning paradigm. In experiments, we show that our approach learns succinct and well-adapted graph topologies, induces effective coordination, and improves performance across a variety of cooperative multi-agent tasks

Restoration of Mangrove Plantations and Colonisation by Native Species in Leizhou Bay, South China

To examine the natural colonization of native mangrove species into remediated exotic mangrove stands in Leizhou Bay, South China, we compared soil physical–chemical properties, community structure and recruitments of barren mangrove areas, native mangrove species plantations, and exotic mangrove species—Sonneratia apetala Buch.Ham—between plantations and natural forest. We found that severely degraded mangrove stands could not regenerate naturally without human intervention due to severely altered local environments, whereas some native species had been recruited into the 4–10 year S. apetala plantations. In the first 10 years, the exotic species S. apetala grew better than native species such as Rhizophora stylosa Griff and Kandelia candel (Linn.) Druce. The mangrove plantation gradually affected soil physical and chemical properties during its recovery. The exotic S. apetala was more competitive than native species and its plantation was able to restore soil organic matter in about 14 years. Thus, S. apetala can be considered as a pioneer species to improve degraded habitats to facilitate recolonization by native mangrove species. However, removal to control proliferation may be needed at late stages to facilitate growth of native species. To ensure sustainability of mangroves in South China, the existing mangrove wetlands must be managed as an ecosystem, with long-term scientific monitoring program in place