Query–service matching in customer service systems faces a critical challenge of accurately aligning user queries expressed in colloquial language with formally defined services while balancing business objectives. Traditional keyword-based and...
The use of structured query formulation languages is a method for gaining access to data in information management systems. Writing structured queries is a powerful method of data retrieval, and end users can generate complex database queries by beco...
Knowledge graphs (KGs) have gained prominence for representing real-world facts, with queries of KGs being crucial for their application. Aggregate queries, as one of the most important parts of KG queries (e.g., “ What is the average price of...
Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless sy...
Artificial intelligence has shown great promise in advancing intelligent surgical systems. Among its applications, surgical video action recognition plays a critical role in enabling accurate intraoperative understanding and decision support. However...
Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-o...
Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluatio...
This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact...
Large language models (LLMs) have driven transformative progress in artificial intelligence, yet critical challenges persist in data management and privacy protection during model deployment and training. The approximate nearest neighbor (ANN) search...
Generating accurate SQL queries from natural language is critical for enabling non-experts to interact with complex databases, particularly in high-stakes domains like healthcare. This paper presents an extensive evaluation of state-of-the-art large...
The trajectory data collected by an Automatic Identification System (AIS) are an essential resource for various ships, and effective filtering and querying approaches are fundamental for managing these data. Natural language has become the preferred...
Large language models (LLMs) incur prohibitive computational costs when deployed as monolithic systems for multi-domain query processing. This paper proposes Accelerated LLM, a modular architecture that replaces a single general-purpose LLM with an e...
Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians’ cognitive burden, yet its effectiveness hinges on query cl...
Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users’ questions. Traditional pipeline approaches manag...
Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks. In black-box environments, adversaries have limited knowledg...
Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input que...
In the current scenario of fast technological advancement, increasingly characterized by widespread adoption of Artificial Intelligence (AI)-driven tools, the significance of autonomous systems like chatbots has been highlighted. Such systems, which...
In the field of Information Retrieval (IR), user-submitted keyword queries often fail to accurately represent users’ true search intent. With the rapid advancement of artificial intelligence, particularly in natural language processing (NLP), q...
In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The singl...
The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data,...
The skyline query processing problem has been well studied for many years. The literature on skyline algorithms so far mainly considers static query points on static attributes. With the popular usage of mobile devices along with the increasing numbe...
By leveraging retrieval-augmented generation technology in human–computer interaction applications, a large language model was established based on local hardware to construct a root cause query model for equipment failures on virtual power pla...
The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials...
Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through r...
Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models gen...
Pharmaceutical wholesalers manage vast catalogues spanning dozens of distributors and heterogeneous nomenclatures, where Customer Service Representatives (CSRs) in call centers must identify products from semantically ambiguous natural language queri...
Question answering over domain-specific knowledge graphs implies several challenges. It requires sufficient knowledge of the world and the domain to understand what is being asked, familiarity with the knowledge graph’s structure to build a cor...
This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such...
In recent years, cross-modal retrieval has garnered significant attention due to its potential to bridge heterogeneous data modalities, particularly in aligning visual content with natural language. Despite notable progress, existing methods often st...
The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from...
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usu...
Large Language Model (LLM) agents depend heavily on multiple external tools such as APIs, databases and computational services to perform complex tasks. However, these tool executions create latency and introduce costs, particularly when agents handl...
In recent years, large language models (LLMs) have shown an impressive ability in translating text to SQL queries. However, in real-world applications, standard loss functions frequently fail to capture the complexity of queries adequately. Therefore...
Multimodal large language models (MLLMs) face substantial memory bottlenecks when processing long visual contexts, such as videos and high-resolution images. Existing methods that allocate visual KV cache budgets using cross-modal attention entropy m...
Fluid migration screening requires the integration of seismic structure and well log response. This study develops a multimodal framework for ranking an attribute-derived fluid migration favorability proxy from paired local seismic patches and well l...
Simulation has become an indispensable technique for modelling and evaluating the performance of large-scale systems efficiently and at a relatively low cost. ElasticSearch (ES) is one of the most popular open source large-scale distributed data inde...
Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applicat...
To address the difficulty of synergizing multi-source spatial data with geological text and the limited spatial reasoning of large language models (LLMs), this paper proposes an intention-driven hybrid semantic–spatial retrieval–augmented...
Cultural heritage inventories have been created to collect and preserve the culture and to allow the participation of stakeholders and communities, promoting and disseminating their knowledges. There are two types of inventories: those who give data...
Remote sensing referring image segmentation faces critical challenges including arbitrary target rotation, drastic scale variation, cluttered complex backgrounds, and large visual-language semantic gaps. Existing mainstream segmentation models adopt...
A sketch map represents an individual’s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities betwe...
Join size estimation plays a crucial role in query optimization, correlation computing, and dataset discovery. A recent study, LDPJoinSketch, has explored the application of local differential privacy (LDP) to protect the privacy of two data sources...
Fine-grained visual classification (FGVC) requires models to distinguish subtle inter-class differences while remaining robust to substantial intra-class variations in pose and background. Hierarchical visual backbones improve semantic abstraction bu...
The industry foundation classes (IFC) data model is the most important data schema in ensuring the interoperability of the information generated throughout the lifecycle of facilities. However, because the current IFC model is focused on buildings, t...
Generalized category discovery (GCD) aims to classify known categories while discovering novel ones in unlabeled data, yet existing methods lack mechanisms to correct boundary-ambiguous samples that receive noisy pseudo-labels, as they primarily rely...
Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analy...
For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for...
Deduplication in storage systems has gained momentum recently for its capability in reducing data footprint. However, deduplication introduces challenges to storage management as storage objects (e.g., files) are no longer independent from each other...
This contribution outlines the design of a Knowledge Hub of heterogeneous documents related to the UNEP/MAP Barcelona Convention system. The Knowledge Hub is intended to serve as a resource to assist public authorities and users with different backgr...
Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The...
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