Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.

The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and other domains involving artificial intelligence. Given the intricate nature of natural scenes, the occurrence of object occlusion and semi-occlusion is commonplace in basic tracking tasks. These factors often result in challenges such as ID switching, object loss, detection errors, and misaligned bounding boxes, thereby significantly impacting the precision of multi-object tracking.This paper aims to address the aforementioned issues and proposes a novel multi-object tracker, incorporating Relative location mapping (RLM) and Target region density (TRD) modeling. The new tracker is more sensitive to differences in the spatial relationships between targets, allowing it to dynamically introduce low-scoring detection boxes into different regions based on the density of target regions in the image. This improves the accuracy of target tracking while avoiding the consumption of a significant amount of computational resources.Our research results indicate that when applying this method to state-of-the-art multi-object tracking approaches, the proposed model achieves improvements of 0.4 to 0.8 points in the HOTA and IDF1 metrics on the MOT17 and MOT20 datasets. This demonstrates the effectiveness of the proposed method in enhancing multi-object tracking performance.

Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.