The proliferation of cloud-based data outsourcing has intensified the need for efficient semantic retrieval over encrypted data. Existing searchable encryption schemes often face a coupled bottleneck: (i) semantic index can be unstable or overly coar...
Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still...
The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based sear...
Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system...
Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate secu...
This paper addresses the challenge of providing operational access to current metadata in complex, ever-changing relational data warehouses. Traditional catalogs struggle to keep up with changes in schemas, code, and processes. The paper presents a m...
In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based...
Collaborative projects under the Horizon Europe Framework Program of the European Union typically involve a large number of partners from multiple countries. Data-centric projects, among them, often require integration of disparate data source format...
Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs...
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mi...
With the increase in the amount of images online, the whole Internet is becoming an image database. Since there are so many available images, it is difficult for users to find the desired images. Unlike text search engines, image search engines canno...
29 October 2026
Sustainability has become central to digital commerce research, yet that literature is rarely confronted with independent evidence of what the public actually attends to. This study pairs two analyses for the Republic of Korea. Analysis 1 is a PRISMA...
Semantic indexing ensures better organization and optimized searching of heterogeneous, autonomous, and distributed data sources. This approach leverages meaning and context rather than just keywords to better manage the increasing volume, complexity...
Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rar...
Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a g...
Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced...
With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to...
In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which shou...
Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the sea...
The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and...
Code search has received significant attention in the field of computer science research. Its core objective is to retrieve the most semantically relevant code snippets by aligning the semantics of natural language queries with those of programming l...
With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photog...
One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and te...
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relations...
For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most i...
The amount of digital cultural heritage data produced by cultural heritage institutions is growing rapidly. Digital cultural heritage repositories have therefore become an efficient and effective way to disseminate and exploit digital cultural herita...
Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discov...
Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilar...
Due to the low costs of its storage and search, the cross-modal retrieval hashing method has received much research interest in the big data era. Due to the application of deep learning, the cross-modal representation capabilities have risen markedly...
Artificial intelligence (AI) has transformed how we engage with visual data, particularly within the context of enterprises. Multi-modal codification systems enable the creation of semantic connections between text and visual data using AI models. Th...
This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the sem...
Geospatial data sharing is an inevitable requirement for scientific and technological innovation and economic and social development decisions in the era of big data. With the development of modern information technology, especially Web 2.0, a large...
The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process itera...
The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched us...
Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between relate...
This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 y...
POI data play an important role in various location-based services, including navigation, positioning, and local search applications. However, as cities rapidly develop, a substantial amount of new POI data are generated daily, often accompanied by i...
Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. Howeve...
In the era of big data, linked data interfaces play a critical role in enabling access to and management of large-scale, heterogeneous datasets. This survey investigates forty-seven interfaces developed by the semantic web community in the context of...
Existing 3D city reconstruction via oblique photography can only produce surface models, lacking semantic information about the urban environment and the ability to incorporate all individual buildings. Here, we propose a method for the semantic segm...
E-commerce search engines often fall short in delivering accurate and fair results due to their reliance on keyword matching and bias towards larger companies. This paper introduces a novel search engine architecture that integrates advanced AI techn...
The ever-increasing quantities of digital photo resources are annotated with enriching vocabularies to form semantic annotations. Photo-sharing social networks have boosted the need for efficient and intuitive querying to respond to user requirements...
Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pu...
Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax...
Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions...
High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and represe...
Information retrieval serves as a critical methodology for accurately and efficiently obtaining the required information from massive amounts of data. In this paper, we propose an information retrieval framework (SE-MSLC) that utilizes information th...
The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Ope...
By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning...
The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform bill...
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