Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a large amount of unstructured text to expand entities and relations. Although a large number of English open relation prediction methods have achieved good performance, the high-performance system for open domain Chinese triples extraction remains undeveloped due to the lack of large-scale Chinese annotation corpora and the difficulty of Chinese language processing. In this paper, we propose an integrated open domain Chinese triples hierarchical extraction method (CTHE) to solve this problem, considering the advantages of Bi-LSTM-CRF and Att-Bi-GRU models based on the pre-trained BERT encoding model. This method can recognize the named entities from Chinese sentences to establish entity pairs, and implement hierarchical extraction of specific and open relations based on the user-defined schema library and attention mechanism. The experimental results demonstrate the effectiveness of this method, which achieved stable performance on the test dataset, and better precision and F1-score in comparison with state-of-the-art Chinese open domain triples extraction methods. Furthermore, a large-scale annotated dataset for a Chinese named entity recognition (NER) task is established, which provides support for research on Chinese NER tasks.

The future query of moving objects involves predicting their future positions based on their current locations and velocities to determine whether they will appear in a specified area. This technique is crucial for positioning and navigation, and its importance in our daily lives has become increasingly evident in recent years. Nonetheless, the growing volume of data renders traditional index structures for moving objects, such as the time-parameterized R-tree (TPR-tree), inefficient due to their substantial storage overhead and high query costs. Recent advancements in learned indexes have demonstrated a capacity to significantly reduce storage overhead and enhance query efficiency. However, most existing research primarily addresses static data, leaving a gap in the context of future queries for moving objects. We propose a novel future query technique for moving objects based on a learned index (FurMoLi for short). FurMoLi encompasses four key stages: firstly, a data partition through clustering based on velocity and position information; secondly, a dimensionality reduction mapping two-dimensional data to one dimension; thirdly, the construction of a learned index utilizing piecewise regression functions; and finally, the execution of a future range query and future KNN query leveraging the established learned index. The experimental results demonstrate that FurMoLi requires 4 orders of magnitude less storage overhead than TPR-tree and 5 orders of magnitude less than $B^{+}$-tree for moving objects ($B^x$-tree). Additionally, the future range query time is reduced to just 41.6% of that for TPR-tree and 34.7% of that for $B^x$-tree. For future KNN queries, FurMoLi’s query time is only 70.1% of that for TPR-tree and 47.4% of that for $B^x$-tree.

How to quickly identify high-quality frontier topics from massive scientific research data to assist researchers in accurately carrying out scientific research work is of great importance. Traditional analysis methods have some bottlenecks, such as weak cross-domain adaptability, high resource consumption and low efficiency. In order to solve the above problems, a frontier topics mining method via AI-agent is proposed. A generative-verification dual-agents (D-Agents) architecture is innovatively constructed. Firstly, prompt engineering is used to construct generative agent (G-Agent), and the semantic understanding ability of large-scale pre-trained language models is used to realize the automatic generation of candidate frontier topics; Then, the verification agent (V-Agent) is introduced to establish a multi-dimensional evaluation system, and the candidate results are automatically verified from the dimensions of academic novelty, topic accuracy and completeness to identify frontier topics. The effectiveness of the proposed method is verified by constructing three labeled test dataset including computer vision (CV), natural language processing (NLP), and machine learning (ML). The experimental results show that D-Agents can be competent for frontier topics mining tasks in multiple domain at the same time. On three manually labeled datasets: CV-DataSet, NLP-DataSet and ML-DataSet, the accuracy rate of D-Agents exceeds 74% while maintaining the coverage rate of more than 85%. Compared with traditional bibliometric methods, the accuracy and coverage rate of frontier topics mining in three different fields: altitude sickness, recommendation system and oyster reef ecosystem have reached more than 67%. It can effectively alleviate the hallucination problem of G-Agent through the automatic generation and self-verification mechanism in D-Agents, and greatly improve the efficiency of frontier topics mining.