Abstract Relation extraction is a research hot-spot in the field of natural language processing, and aims at structured knowledge acquirement. However, existing methods still grapple with the issue of entity overlapping, where they treat relation types as inconsequential labels, overlooking the fact that relation type has a great influence on entity type hindering the performance of these models from further improving. Furthermore, current models are inadequate in handling the fine-grained aspect of entity positioning, which leads to ambiguity in entity boundary localization and uncertainty in relation inference, directly. In response to this challenge, a relation extraction model is proposed, which is guided by relational semantic cues and focused on entity boundary localization. The model uses an attention mechanism to align relation semantics with sentence information, so as to obtain the most relevant semantic expression to the target relation instance. It then incorporates an entity locator to harness additional positional features, thereby, enhancing the capability of the model to pinpoint entity start and end tags. Consequently, this approach effectively alleviates the problem of entity overlapping. Extensive experiments are conducted on the widely used datasets NYT and WebNLG. The experimental results show that the proposed model outperforms the baseline ones in F1 scores of the two datasets, and the improvement margin is up to 5.50% and 2.80%, respectively.

Multi-Hop Question Answering (MHQA), crucial for complex information retrieval, remains challenging for current Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, which often suffer from hallucination, reliance on incomplete knowledge, and opaque reasoning processes. Existing RAG methods, while beneficial, still struggle with the intricacies of multi-step inference and ensuring verifiable accuracy. This research introduces TreeQA, a novel framework designed to significantly enhance the reliability and interpretability of LLM-RAG systems in MHQA tasks. TreeQA addresses these limitations by decomposing complex multi-hop questions into a hierarchical logic tree of simpler, verifiable sub-questions, integrating evidence from both structured knowledge bases (e.g., Wikidata) and unstructured text (e.g., Wikipedia), and employing an iterative, evidence-based validation and self-correction mechanism at each reasoning step to dynamically rectify errors and prevent their accumulation. Extensive experiments on four benchmark datasets (WebQSP, QALD-en, AdvHotpotQA, and 2WikiMultiHopQA) demonstrate TreeQA’s superior performance, achieving Hit@1 scores of 87%, 57%, 53%, and 59%, respectively, representing improvements of 4%-12% over state-of-the-art LLM-RAG methods. These findings highlight the significant impact of structured, verifiable reasoning pathways in developing more robust, accurate, and interpretable knowledge-intensive AI systems, thereby enhancing the practical utility of LLMs in complex reasoning scenarios. Our code is publicly available at https://github.com/ACMISLab/TreeQA .

Summary Machine learning (ML) has been widely applied in many areas in recent decades. However, because of its inherent complexity and characteristics, the efficiency and effectiveness of ML algorithm often to be heavily relies on the technical experts' experience and expertise which play a crucial role to optimize hyperparameters of algorithms. Generally, the procedure tuning the exposed hyperparameters of ML algorithm to achieve better performance is called Hyperparameters Optimization. Traditional hyperparameters optimization methods are manually exhaustive search, which is unavailable for high dimensional search spaces and large datasets. Recent automated sequential model‐based optimization led to substantial improvements for this problem, whose core idea is fitting a regression model to describe the importance and dependence of algorithm's performance on certain given hyperparameter setting. Sequential model‐based algorithm configuration (SMAC) is a the‐state‐of‐art approach, which is specified by four components, Initialize, FitModel, SelectConfigurations, and Intensify. In this article, we propose to add a pruning procedure into SMAC approach, it quantifies the importance of hyperparameters by analyzing the performance of a list of promising configurations and reduces search space by discarding noncritical and bad key hyperparameters. To investigate the impact of pruning for model's performance, we conducted experiments on the configuration space constructed by Auto‐Sklearn and compared the effect of run time and pruning ratio with our algorithm. The experiments results verified that, our method made the configuration selected by SMAC more stable and achieved better performance.

SUMMARY With the rapid developments in video processing technologies, video data have increased rapidly and become popular in our daily life for both professional and consumer applications such as surveillance, education and entertainment. Because of the increasing processing workload, more and more queries with expensive video predicates are being implemented in a parallel environment for better performance. Such requirements entail that the data management system not only be able to store and access video content, but also be able to optimize queries that have expensive video predicates in an effective and efficient way in a cloud environment. In previous research literatures, parallel and distributed policies and query optimizations in relational database management systems are often based on the disk input/output (I/O) cost of involved operations and network transmission cost. However, for a query that contains expensive video predicates in a cloud environment, the traditional cost estimation model does not work well. Although researchers have proposed some approaches that can solve the problem in certain situations, there are still some unresolved issues, and these approaches need further optimizations. This paper is motivated by a real‐world large supermarket business data and video surveillance data management scenario in a parallel environment. By considering the characteristics of video data and their expensive processing, we present methods named operating results buffer and operating results buffer‐C for implementing expensive video predicates at simple node, mapping video data and executing expensive video predicates in a cloud environment, which reduce the cost of video data transmission and the invoking times of expensive video predicates. We propose a novel query optimization approach that reconstructs the join order‐based estimation for attribute cardinality and computes the total cost with I/O, network and expensive processing. This approach reduces the invoking times of expensive video predicates to a greater degree and gives a better solution for mixed query optimization, which contains traditional data types and large object operations in a cloud environment. Our query performance improves by 30% to 80% compared with existing expensive predicates query optimization methods. Copyright © 2011 John Wiley & Sons, Ltd.

Video has become popular in our daily life for both professional and consumer applications. Both low level video processing and high level semantic video analysis are critically computational tasks in application domains. Most of current video computing tools are developed for specific analytic tasks, they are lack higher level interoperability with database and treat database merely as a relational data storage engine rather than an analytic platform, which causes inefficient data access and massive amount of data movement. In this paper, we study how to support video data management over relational database, and present our initial solutions of video data storage mechanism, video data access method and efficient video analytics. We also illustrate our ongoing prototype system HybVideo that developed in a novel architecture. It integrates above solutions to tackle the major challenges of providing a platform for both storage and analysis of video data.