With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.

Novelty search, which was inspired by the nature that evolves creatures with diversity, has shown great potential in solving reinforcement learning (RL) tasks with sparse and deceptive rewards. However, most of the existing novelty search methods evolve the populations through hybrization and mutation, which is inefficient in diverging populations. In this paper, we propose a method which incorporates deep RL with novelty search to improve the efficiency of diverging the populations for novelty search. We first propose a strategy that improves the novelty of individuals generated by genetic algorithm using reinforcement learning. Based on this strategy, we propose a framework that incorporates deep RL with novelty search, and then derive an algorithm to improve the search efficiency of the novelty search for continuous control tasks. Our experimental results show that our method can improve the search efficiency of novelty search and can also provide a competitive performance compared to some of the existing novelty search methods. The implementation of our method is available at: https://github.com/shilx001/NoveltySearch_Improvement .

Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA $$^3$$3 D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.

Deep reinforcement learning (DRL), has shown promise in solving intractable challenges in interactive recommendation systems. In DRL-based interactive recommendation, state modeling is crucial for well-capturing users’ continuous interaction behaviors with shopping systems. A user’s multiple continuous interactions in a given time period (e.g., the time from login to log out) naturally constitute a session. However, existing studies often overlook such valuable session structure and characteristics and instead simply treat them as sequences. As a result, they are not able to capture the complex transitions over users’ interactions within or between sessions, leading to significant information loss. To bridge this significant gap, in this paper, we propose Session-based Interactive Recommendation with Graph Neural Networks (SIR-GNN). SIR-GNN models interaction data as sessions and employs novel graph neural networks to capture rich transition patterns among interactions. Specifically, a novel 3-level transition module is well designed to effectively capture common patterns from all sessions, intra-session transitions, and adjacent-item transitions respectively, followed by an attention-based gated graph neural network to model the state representation for SIR well. Extensive experiments on 3 real-world benchmark datasets demonstrate the superiority of SIR-GNN over state-of-the-art baselines and the rationality of our design in SIR-GNN.

The popularity of mobile games has surged in recent years. Along with mobile games, the emergence of minigames has recently raised attention. Compared to traditional mobile games, mini-games are more lightweight and platformindependent with low development cost, which has attracted thousands of developers and users. WeChat mini-games platform is one of the most popular platforms with over 100,000 minigames. The diversity and variety of WeChat mini-games make it an ideal platform for training reinforcement learning (RL) agents. In contrast, most of the existing RL benchmark environments are equipped with predetermined games, which are always limited to several genres and lack the utilization of new and diverse games. To utilize the WeChat mini-games for RL research, in this paper, we propose WemiEnv, a lightweight, easy-to-use and open-source platform for reinforcement learning research towards WeChat mini-games. WemiEnv is built on the WeChat developer tools and allows RL agents to interact with the mini-games. WemiEnv also supports user-customized mini-games, requiring users to implement only a few interface functions within WemiEnv API. We also provide 6 popular mini-games: Space Fighter, Flip, 2048, Flappy Bird, Timberman and Snake as ready-to-use tasks. Experiments were conducted with the OpenAI Spinning Up library for RL baselines on the provided tasks to test the usability of WemiEnv. Our code and documentation are available at https://github.com/Ada9io/WemiEnv.